Relevant bibliographies by topics / Metoda PCA (Principal Component Analysis)

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Academic literature on the topic 'Metoda PCA (Principal Component Analysis)'

Author: Grafiati
Published: 24 April 2022
Last updated: 27 April 2022

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Journal articles on the topic "Metoda PCA (Principal Component Analysis)"

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Suhery, Cucu, and Ikhwan Ruslianto. "Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA)." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 3, no. 1 (April 17, 2017): 9. http://dx.doi.org/10.26418/jp.v3i1.19792.

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Berbagai sistem monitoring presensi yang ada memiliki kekurangan dan kelebihan masing-masing, dan perlu untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya. Pada penelitian ini dikembangkan suatu sistem monitoring presensi menggunakan deteksi wajah manusia yang diintegrasikan dengan basis data menggunakan bahasa pemrograman Python dan library opencv. Akuisisi data citra dilakukan dengan ponsel android, kemudian citra tersebut dideteksi dan dipotong sehingga hanya didapat bagian wajah saja. Deteksi wajah menggunakan metode Haar-Cascade Classifier, kemudian ekstraksi fitur dilakukan menggunakan metode Principal Component Analysis (PCA). Hasil dari PCA diberi label sesuai dengan data manusia yang ada pada basis data. Semua citra yang telah memiliki nilai PCA dan tersimpan di basis data akan dicari kemiripannya dengan citra wajah pada proses pengujian menggunakan metoda Euclidian Distance. Pada penelitian ini basis data yang digunakan yaitu MySQL. Hasil deteksi citra wajah pada proses pelatihan memiliki tingkat keberhasilan 100% dan hasil identifikasi wajah pada proses pengujian memiliki tingkat keberhasilan 90%.. Kata kunci— android, haar-cascade classifier, principal component analysis, euclidian distance, MySQL, sistem monitoring presensi, deteksi wajah
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Murdika, Muhammad Alif, Yessi Mulyani, Umi. "Identifikasi Kualitas Buah Tomat dengan Metode PCA (Principal Component Analysis) dan Backpropagation." Electrician 15, no. 3 (October 25, 2021): 175–80. http://dx.doi.org/10.23960/elc.v15n3.2240.

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Intisari — Analisis komponen utama atau principal component analysis merupakan suatu metode yang digunakan dalam menganalisis kumpulan dataset untuk meringkas karakteristik utama mereka. Metode PCA ini mengurangi dimensi dataset dengan memproyeksikan setiap titik data ke hanya beberapa komponen utama pertama untuk mendapatkan data berdimensi lebih rendah sambil mempertahankan sebanyak mungkin variasi data. Pada penelitian ini digunakan metode PCA untuk memproyeksikan data citra sehingga diperoleh data ekstraksi ciri dengan dimensi yang lebih kecil. Selanjutnya metode Backpropagation diterapkan untuk melakukan proses identifikasi. Dataset yang digunakan sebanyak 30 dataset yang terdiri dari 10 buah citra uji dan 20 data latih. Dari simulasi yang dilakukan diperoleh kesimpulan bahwa Metode PCA yang diterapkan berhasil mengurangi dimensi data. Identifikasi kualitas buah tomat menggunakan metode Back-propagation menunjukkan tingkat ketepatan dengan akurasi mencapai 76,7%. Nilai akurasi tersebut menunjukkan bahwa sistem ini telah berjalan dengan baik. Kata kunci — Back-propagation, PCA (Principal Componenet Analisys), Pengolahan Citra, JST (Jaringan Syaraf Tiruan). Abstract — Principal component analysis is a method used in analyzing datasets to summarize their main characteristics. This PCA method reduces the dimension of the dataset by projecting each data point onto only the first few principal components to obtain lower dimensional data while maintaining as much variation of the data as possible. In this study, the PCA method was used to project image data in order to obtain feature extraction data with smaller dimensions. Furthermore, the Backpropagation method is applied to carry out the identification process. The dataset used is 30 data consisting of 10 test images data and 20 training data. From the simulation, it can be concluded that the PCA method applied has succeeded in reducing the dimensions of the data. Identification of tomato fruit quality using the Back-propagation method shows the level of accuracy with an accuracy of 76.7%. it indicates that this system has been running well. Keywords — Backpropagation, PCA (Principal Component Analysis), Image Processing, ANN (Artificial Neural Networks).
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Tinungki, Georgina M., and Nurtiti i. Sunusi. "Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 6, 2018): 42. http://dx.doi.org/10.20956/jmsk.v15i2.5568.

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Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan.
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Tinungki, Georgina M., and Nurtiti Sunusi. "Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 20, 2018): 44. http://dx.doi.org/10.20956/jmsk.v15i2.5713.

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Abstract Sparse Principal Component Analysis (Sparse PCA) is one of the development of PCA. Sparse PCA modifies new variables as a linier combination of p old variables (original variable) which is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA. In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements (loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse
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Rosyani, Perani. "Pengenalan Wajah Menggunakan Metode Principal Component Analysis (PCA) dan Canberra Distance." Jurnal Informatika Universitas Pamulang 2, no. 2 (June 25, 2017): 118. http://dx.doi.org/10.32493/informatika.v2i2.1515.

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Wajah merupakan salah satu karakteristik biometrik yang digunakan untuk mengenali seseorang selain karakteristik yang lain seperti ucapan; sidik jari; retina; dll. Wajah adalah struktur multidimesi yang sangat kompleks dan membutuhkan tehnik komputasi yang baik untuk pengenalan. Di dalam penelitian ini; penulis mengambil 10 pose wajah yang berbeda kemudian menggunakan metode PCA untuk pengoptimalan dalam mereduksi dimensi. Setelah citra original di ekstraksi menggunakan PCA maka akan di hitung tingkat kesamaan (similarity degree) antara gambar test dengan gambar training menggunakan metode jarak. Hasil dari penelitian ini adalah tingkat kesamaan yang dihasilkan setelah proses reduksi dan ekstraksi menggunakan PCA didapatkan rata-rata nilai untuk Canbera Distance adalah 77;59.
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Yusni, Romi mulyadi, and Zaini. "Identifikasi Pengenalan Wajah Perokok Menggunakan Metode Principal Component Analysis." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (October 30, 2020): 892–98. http://dx.doi.org/10.29207/resti.v4i5.2272.

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Cigarettes are one of the biggest contributors to preventable causes of death in society. Cigarette smoke contains various chemicals that can cause various diseases such as chronic coughs, lung cancer, and other health problems. Cigarette smoke not only harms the health of the smoker itself but also the health of others. Sometimes written warnings about smoking bans are often not followed by active smokers. This study aims to identify smokers 'facial recognition in order to recognize and identify smokers' faces who do not obey the rules by using dimensional reduction techniques oriented to the Principal component Analysis (PCA) method. Principal Component Analysis will later be integrated with the Eigenface and Eucladean analysis algorithms to reduce the image size in obtaining the best value vectors to simplify the face image in the input image space and look for the threshold value which is the threshold that the test data must pass so that it can prove the data value. testing becomes recognizable data through the calculation of the distance for each weight. In this study, there were 8 smoker faces with 5 different facial poses that were tested for 40 face recognition experiments and resulted in 34 correct smoker face recognition and 6 wrong smoker face recognition with an accuracy rate of 92.5% and a long face recognition process time of 80. second. This test has proven that the Eigenface and Euclidean distance in the Principal Component Analysis (PCA) are able to handle and recognize smoker's facial image data well.
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Harizahayu, Harizahayu. "PENGENALAN EKSPRESI RAUT WAJAH BERBASIS JARINGAN SARAF TIRUAN BACKPROPAGATION DENGAN METODE PRINCIPAL COMPONENT ANALYSIS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 1 (March 1, 2021): 037–46. http://dx.doi.org/10.30598/barekengvol15iss1pp037-046.

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The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.
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Susilowati, Bekti Endar, and Pardomuan Robinson Sihombing. "Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier." Jurnal Ilmu Komputer 13, no. 2 (September 28, 2020): 11. http://dx.doi.org/10.24843/jik.2020.v13.i02.p04.

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Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster.
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Faroek, Dewi Astria, Rusydi Umar, and Imam Riadi. "Deteksi Keaslian Citra Menggunakan Metode Error Level Analysis (ELA) dan Principal Component Analysis (PCA)." Format : Jurnal Ilmiah Teknik Informatika 8, no. 2 (February 4, 2020): 132. http://dx.doi.org/10.22441/format.2019.v8.i2.006.

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Kemajuan teknologi yang ada pada citra digital mempengaruhi banyak kemungkinan pada perangkat pencitraan dengan resolusi yang tinggi dengan biaya yang rendah. Hal ini dapat dimanfaatkan oleh pihak-pihak tertentu dalam memanipulasi citra digital agar lebih baik hingga sangat jauh dari hasil citra aslinya. Pemalsuan citra adalah proses manipulasi pada sebagian atau seluruh daerah citra baik terhadap isi maupun konteks citra dengan bantuan teknik pemrosesan citra digital. Dengan manipulasi citra ini banyak pihak yang dapat melakukan sebuah tindakan kejahatan. Definisi forensik citra merupakan bidang ilmu yang digunakan untuk mengidentifikasi asal dan menverifikasi keaslian sebuah citra tersebut. Hal yang mendasari dalam melakukan deteksi keaslian citra adalah melakukan perbandingan antara dua image dan dua metode yaitu metode Error Level Analysis (ELA) dan Principal Component Analysis (PCA) dengan menggunakan tools forensically-beta. Penelitian ini diharapkan menunjukkan hasil yang baik dalam mendeteksi objek pada citra sehingga dapat membantu dalam mendeteksi citra yang asli dan citra yang telah dimanipulasi berdasarkan metode ELA dan PCA.
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Suryaman, Sean Alexander, Rita Magdalena, and Sofia Sa'idah. "Klasifikasi Cuaca Menggunakan Metode VGG-16, Principal Component Analysis Dan K-Nearest Neighbor." Jurnal Ilmu Komputer dan Informatika 1, no. 1 (August 28, 2021): 1–8. http://dx.doi.org/10.54082/jiki.1.

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Cuaca merupakan suatu fenomena alam yang sangat berdampak bagi manusia. Informasi tentang kondisi cuaca sangat dibutuhkan oleh manusia. Informasi ini sangat bermanfaat untuk mengetahui kejadian cuaca disekitar kita. Sistem klasifikasi saat ini mengandalkan serangkaian sensor mahal atau bantuan manusia. Kecerdasan buatan merupakan suatu cabang ilmu komputer yang membantu manusia dalam mengatasi masalah yang ada. Penelitian ini menggunakan kecerdasan buatan untuk mengklasifikasi kondisi cuaca dengan menggunakan metode VGG-16, Principal Component Analysis (PCA) dan K-Nearest Neighbor (KNN). Pertama ciri akan dicari menggunakan VGG-16, lalu memanfaatkan Principal Component Analysis (PCA) untuk mereduksi data agar lebih efektif. Dan menggunakan K-Nearest Neighbor (KNN) untuk mengklasifikasian data. K-Nearest Neighbor (KNN) menggunakan jarak untuk mengklasifikasikan data. Jarak yang dipilih merupakan jarak terpendek yang akan menunjukan ketetanggan untuk menghasilkan keluaran apakah cuaca sedang cerah, berawan, berkabut, hujan dan matahari terbit. Sistem tersebut dibuat menggunakan platform Google Colab dengan bahasa pemrograman Python. Berdasarkan hasil penelitian, diperoleh sistem klasifikasi cuaca dengan akurasi sebesar 87,50%. Hasil akurasi tersebut diperoleh ketika digunakan 450 data uji dan 1050 data latih. Adapun parameter terbaik yang dihasilkan, yaitu ukuran citra 256 x 256, jenis KNN adalah Cosine, nilai KNN di k = 9, dan Persentase PCA 30%.
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Dissertations / Theses on the topic "Metoda PCA (Principal Component Analysis)"

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Geschwinder, Lukáš. "Možnosti využití metod vícerozměrné statistické analýzy dat při hodnocení spolehlivosti distribučních sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217824.

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The aim of this study is evaluation of using multi-dimensional statistical analyses methods as a tool for simulations of reliability of distribution network. Prefered methods are a cluster analysis (CLU) and a principal component analysis (PCA). CLU is used for a division of objects on the basis of their signs and a calculation of the distance between objects into groups whose characteristics should be similar. The readout can reveal a secret structure in data. PCA is used for a location of a structure in signs of multi-dimensional matrix data. Signs present separate quantities describing the given object. PCA uses a dissolution of a primary matrix data to structural and noise matrix data. It concerns the transformation of primary matrix data into new grid system of principal components. New conversion data are called a score. Principal components generating orthogonal system of new position. Distribution network from the aspect of reliability can be characterized by a number of new statistical quantities. Reliability indicators might be: interruption numbers, interruption time. Integral reliability indicators might be: system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). In conclusion, there is a comparison of performed SAIFI simulation according to negatively binomial division and provided values from a distribution company. It is performed a test at description of sign dependences and outlet divisions.
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Šrenk, David. "Vizualizace spektroskopických dat pomocí metody analýzy hlavních komponent." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401532.

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This diploma thesis deals with using laser-induced breakdown plasma spectroscopy for determining the elemental structure of unknown samples. It was necessary to design an appropriate method to qualify material by laser-induced emission spectrum. Pretreatment of data and using a variety of chemometrics methods had to be done in order to qualify the structure of elements. We achieved a required solution by projecting the data to a new PCA space, creating clusters and computing the Euclidean distance between each cluster. The experiment in the practical part was set to detect an interface of two elements. We created a data file simulating the ablation on the interface. This data set was gradually processed applying a mathematical-chemical-physical view. Several data procedures have been compiled: approximation by Lorenz, Gauss and Voigt function and also a pretreatment method such as the detection of outliers, standardization by several procedures and subsequent use of principal components analysis. A summarization of processes for input data is fully described in the thesis.
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Ergin, Emre. "Investigation Of Music Algorithm Based And Wd-pca Method Based Electromagnetic Target Classification Techniques For Their Noise Performances." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611218/index.pdf.

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Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres. Small scale aircraft targets modeled by thin conducting wires will also be used in classifier design demonstrations.
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Raiford, Douglas Whitmore III. "Multivariate Analysis of Prokaryotic Amino Acid Usage Bias: A Computational Method for Understanding Protein Building Block Selection in Primitive Organisms." Wright State University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=wright1133886196.

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Lahlou, Mehdi, and Sebastian Sandstedt. "Where There’s Smoke, There’s Fire : An Analysis of the Riksbank’s Interest Setting Policy." Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-143163.

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We analyse the Swedish central bank, the Riksbank’s, interest setting policy in a Taylor rule framework. In particular, we examine whether or not the Riksbank has reacted to fluctuations in asset prices during the period 1995:Q1 to 2016:Q2. This is done by estimating a forward-looking Taylor rule with interest rate smoothing, augmented with stock prices, house prices and the real exchange rate, using IV GMM. In general, we find that the Riksbank’s interest setting policy is well described by a forward-looking Taylor rule with interest rate smoothing and that the use of factors as instruments, derived from a PCA, serves to alleviate the weak-identification problem that tend to plague GMM. Moreover, apart from finding evidence that the Riksbank exhibit a substantial degree of policy rate inertia and has acted so as to stabilize inflation and the real economy, we also find evidence that the Riksbank has been reacting to fluctuations in stock prices, house prices, and the real exchange rate.
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Wang, Tengyao. "Spectral methods and computational trade-offs in high-dimensional statistical inference." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/260825.

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Spectral methods have become increasingly popular in designing fast algorithms for modern highdimensional datasets. This thesis looks at several problems in which spectral methods play a central role. In some cases, we also show that such procedures have essentially the best performance among all randomised polynomial time algorithms by exhibiting statistical and computational trade-offs in those problems. In the first chapter, we prove a useful variant of the well-known Davis{Kahan theorem, which is a spectral perturbation result that allows us to bound of the distance between population eigenspaces and their sample versions. We then propose a semi-definite programming algorithm for the sparse principal component analysis (PCA) problem, and analyse its theoretical performance using the perturbation bounds we derived earlier. It turns out that the parameter regime in which our estimator is consistent is strictly smaller than the consistency regime of a minimax optimal (yet computationally intractable) estimator. We show through reduction from a well-known hard problem in computational complexity theory that the difference in consistency regimes is unavoidable for any randomised polynomial time estimator, hence revealing subtle statistical and computational trade-offs in this problem. Such computational trade-offs also exist in the problem of restricted isometry certification. Certifiers for restricted isometry properties can be used to construct design matrices for sparse linear regression problems. Similar to the sparse PCA problem, we show that there is also an intrinsic gap between the class of matrices certifiable using unrestricted algorithms and using polynomial time algorithms. Finally, we consider the problem of high-dimensional changepoint estimation, where we estimate the time of change in the mean of a high-dimensional time series with piecewise constant mean structure. Motivated by real world applications, we assume that changes only occur in a sparse subset of all coordinates. We apply a variant of the semi-definite programming algorithm in sparse PCA to aggregate the signals across different coordinates in a near optimal way so as to estimate the changepoint location as accurately as possible. Our statistical procedure shows superior performance compared to existing methods in this problem.
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Thorstensson, Linnea. "Clustering Methods as a Recruitment Tool for Smaller Companies." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273571.

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With the help of new technology it has become much easier to apply for a job. Reaching out to a larger audience also results in a lot of more applications to consider when hiring for a new position. This has resulted in that many big companies uses statistical learning methods as a tool in the first step of the recruiting process. Smaller companies that do not have access to the same amount of historical and big data sets do not have the same opportunities to digitalise their recruitment process. Using topological data analysis, this thesis explore how clustering methods can be used on smaller data sets in the early stages of the recruitment process. It also studies how the level of abstraction in data representation affects the results. The methods seem to perform well on higher level job announcements but struggles on basic level positions. It also shows that the representation of candidates and jobs has a huge impact on the results.
Ny teknologi har förenklat processen för att söka arbete. Detta har resulterat i att företag får tusentals ansökningar som de måste ta hänsyn till. För att förenkla och påskynda rekryteringsprocessen har många stora företag börjat använda sig av maskininlärningsmetoder. Mindre företag, till exempel start-ups, har inte samma möjligheter för att digitalisera deras rekrytering. De har oftast inte tillgång till stora mängder historisk ansökningsdata. Den här uppsatsen undersöker därför med hjälp av topologisk dataanalys hur klustermetoder kan användas i rekrytering på mindre datauppsättningar. Den analyserar också hur abstraktionsnivån på datan påverkar resultaten. Metoderna visar sig fungera bra för jobbpositioner av högre nivå men har problem med jobb på en lägre nivå. Det visar sig också att valet av representation av kandidater och jobb har en stor inverkan på resultaten.
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Solomon, Mary Joanna. "Multivariate Analysis of Korean Pop Music Audio Features." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617105874719868.

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Valtancoli, Elena. "Analisi di frequenza a scala regionale degli estremi idrometrici osservati nel Distretto Idrografico del Fiume Po." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22472/.

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Il presente lavoro di Tesi si concentra sullo studio dei metodi di regionalizzazione dell’informazione idrometrica, ovvero sulla cosiddetta analisi regionale di frequenza degli estremi idrologici. Tali metodi consistono nel trasferimento delle informazioni idrometriche osservate (ovvero misure di portata al colmo di piena) dai bacini idrografici vicini al bacino di interesse. In tal modo è possibile aumentare l’accuratezza della stima delle variabili idrologiche di progetto nei siti sprovvisti, o scarsamente dotati, di osservazioni. In letteratura sono riportati numerosi metodi di regionalizzazione, nel presente studio viene presentato e analizzato l’approccio della regione di influenza (Region of Influence, RoI). Tale metodo considera l’influenza di fattori climatici e geomorfologici sui processi idrologici responsabili della formazione dei deflussi. Di conseguenza, dopo aver proceduto alla valutazione in ambiente GIS di tali descrittori, per ogni sito presente nel distretto idrografico del fiume Po, essi sono stati impiegati all’interno del RoI in diverse modalità: prima utilizzando un numero minimo di descrittori e poi mediante l’impiego di analisi statistiche (Principal Component Analysis e Canonical Correlation Analysis), al fine di estrarre indici sintetici su cui valutare l’affinità idrologica tra i diversi bacini e quello di interesse. Ottenendo così il campione regionale RoI e un’opportuna distribuzione di probabilità, da cui ricavare la stima della portata di progetto con assegnata probabilità di superamento (tempo di ritorno). L’applicazione delle diverse metodologie RoI non ha evidenziato la prevalenza di una metodologia rispetto all’altra, tranne nel caso in cui per la valutazione di questi indici vengano impiegati i momenti statistici estratti a partire dai dati di portata, in questo caso il metodo RoI porta alla valutazione di raggruppamenti caratterizzati da una elevata omogeneità, e quindi una migliore stima delle portate di progetto.
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Solat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.

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The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals.
Ph. D.
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Books on the topic "Metoda PCA (Principal Component Analysis)"

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Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.
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Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theory. In particular, the article considers single asset returns, multivariate distribution of returns, risk and portfolio theory, and nonequal time correlations and more general rectangular correlation matrices. It also presents several RMT results on the bulk density of states that can be obtained using the concept of matrix freeness before concluding with a description of empirical correlation matrices of stock returns.
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Book chapters on the topic "Metoda PCA (Principal Component Analysis)"

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Grané, Aurea, and Agnieszka Jach. "Applications of principal component analysis (PCA) in food science and technology." In Mathematical and Statistical Methods in Food Science and Technology, 55–86. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118434635.ch05.

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Grané, Aurea, and Agnieszka Jach. "Applications of principal component analysis (PCA) in food science and technology." In Mathematical and Statistical Methods in Food Science and Technology, 55–86. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118434635.ch5.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision, 1–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_649-1.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision, 636–39. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_649.

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Ruby-Figueroa, René. "Principal Component Analysis (PCA)." In Encyclopedia of Membranes, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_1999-1.

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Bisong, Ekaba. "Principal Component Analysis (PCA)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 319–24. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_26.

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Guebel, Daniel V., and Néstor V. Torres. "Principal Component Analysis (PCA)." In Encyclopedia of Systems Biology, 1739–43. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1276.

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Trendafilov, Nickolay, and Michele Gallo. "Principal component analysis (PCA)." In Multivariate Data Analysis on Matrix Manifolds, 89–139. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76974-1_4.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision, 1013–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_649.

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Lê Cao, Kim-Anh, and Zoe Marie Welham. "Principal Component Analysis (PCA)." In Multivariate Data Integration Using R, 109–36. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003026860-12.

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Conference papers on the topic "Metoda PCA (Principal Component Analysis)"

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Wang, Qianqian, Quanxue Gao, Xinbo Gao, and Feiping Nie. "Angle Principal Component Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/409.

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Recently, many ℓ1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed. Angle PCA employs ℓ2-norm to measure reconstruction error and variance of projected da-ta and maximizes the summation of ratio between variance and reconstruction error of each data. Angle PCA not only is robust to outliers but also retains PCA’s desirable property such as rotational invariance. To solve Angle PCA, we propose an iterative algorithm, which has closed-form solution in each iteration. Extensive experiments on several face image databases illustrate that our method is overall superior to the other robust PCA algorithms, such as PCA, PCA-L1 greedy, PCA-L1 nongreedy and HQ-PCA.
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Hong, Soonyoung, and M. H. Herman Shen. "A Novel Online Structure Damage Identification Using Principal Component Analysis (PCA)." In ASME 2007 Power Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/power2007-22198.

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A novel online structure damage identification using Principal Component Analysis (PCA) techniques and the perceptron backpropagation neural network is presented. There are three phases to execute this method. In Phase I, system modal information, frequencies and mode shapes, are calculated. Phase II is for damage location identification; the Residual Force Vectors (RFVs) are computed as input to the first neural network. Then the network was trained to simulate damage location identification. Phase III is the severity identification step. The PCA method is used to modify the input for the second neural network. Then this network identifies the severity. There are three advantages of using the PCA method, First, PCA method characterizes the original modal information precisely. Second, PCA method creates the unique data for different damage cases unlike other modal property based data. Third, the accuracy of the damage identification improves significantly, when compared with previously developed methods. This method can be operated online for the real time structural damage identification.
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Kepceoğlu, Abdullah, Yasemin Gündoğdu, Kenneth William David Ledingham, and Hamdi Sukur Kilic. "Identification of the isomers using principal component analysis (PCA) method." In 9TH INTERNATIONAL PHYSICS CONFERENCE OF THE BALKAN PHYSICAL UNION (BPU-9). AIP Publishing LLC, 2016. http://dx.doi.org/10.1063/1.4944149.

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Tonshal, Basavaraj, Yifan Chen, and Pietro Buttolo. "Determine Mesh Orientation by Voxel-Based Principal Component Analysis." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99380.

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In this paper we propose a new method to determine the part orientation of a 3D mesh based on Principal Component Analysis (PCA). Although the idea and practice of using PCA to determine part orientation is not new, it is not without practical issues. A major drawback of PCA, when it comes to dealing with meshes comprised of nodes and elements, is that the results are tessellation-dependent because of its sensitivity to variability. Two CAE meshes derived from the same CAD model but with different mesh node distribution characteristics, for instance, can yield different principal components. This is an undesirable outcome because the primary concern in model reorientation is shape, not the representational details of the shape. In order to reduce the influence of node characteristics, weight factors were proposed in the past, but the improvement is limited. To overcome this limitation, we must eliminate the influence of mesh node distribution. We achieve this by introducing an intermediate workspace, which is subsequently voxelized. We then find the intersection of the mesh model with the voxelized workspace. We collect the intersecting voxels to form an intermediate, tessellation-independent representation of the mesh. Applying PCA to this “neutralized” representation allows us to achieve mesh-property-independent results. The voxel representation also provides an opportunity of computational efficiency. We implemented an octree data structure to store the voxels and implemented a fast intersection (between a mesh element and a voxel) check procedure utilizing the interval overlap check derived from the separating axis theorem. Practical issues concerning determination of the voxel space resolution is addressed. A two-step trial and correction approach is proposed to enhance the consistency of results. Our voxel-based PCA is robust, fast, and straightforward to implement. Application examples are shown demonstrating the effectiveness and efficiency of this approach.
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Ermatita, Ika Nurlaili Isnainiyah, Yulnelly Yulnelly, and Amalia Nurul Balqis. "Usability Analysis using Principal Component Analysis (PCA) Method for Online Fish Auction Application." In 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2019. http://dx.doi.org/10.1109/icimcis48181.2019.8985225.

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Choi, Jaewon, and Michael D. Bryant. "Diagnostics of Mechanical Faults of Loudspeakers Using Principal Component Analysis and Fisher’s Discriminant Analysis." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6198.

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This study illustrates a novel model based FDI method for the common mechanical faults arising during the manufacture of loudspeakers. To overcome the drawbacks of the conventional signal based approaches, the Bayesian classification of impulse responses based on a model based fault symptom database is proposed. The loudspeaker model is estimated via IRES and ARMA techniques. The fault symptom database is constructed with a novel nonlinear loudspeaker model. The performances of Principal Component Analysis (PCA) and Fisher’s Discriminant Analysis (FDA) are compared. The results show the effectiveness of the proposed method. It is also shown that the FDA based classifier performs better than PCA in terms of the accuracy and consistency of the healthy baseline estimation. However, the fault isolation is difficult due to the similarities of fault signatures.
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Khaled, Nassim, and Ahmad Smaili. "Curve Representation Using Principal Component Analysis for Shape Optimization of Path Generating Mechanisms." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85247.

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The focus of this paper is on the synthesis of path generation mechanisms based on shape optimization. The principle component analysis (PCA) technique used in image processing is employed to represent the desired coupler curve of the mechanism and simulated annealing is used as the optimization tool. PCA representation is invariant under rotation, translation, scaling, and starting point. Once a shape-optimized mechanism is found, it is translated, rotated, and scaled to its final form. An illustrative example is introduced to demonstrate the proposed method.
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Alozie, Ogechukwu, Yi-Guang Li, Pericles Pilidis, Yang Liu, Xin Wu, Xingchao Shong, Wencheng Ren, and Theodosios Korakianitis. "An Integrated Principal Component Analysis, Artificial Neural Network and Gas Path Analysis Approach for Multi-Component Fault Diagnostics of Gas Turbine Engines." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15740.

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Abstract Gas path diagnostics is a key aspect of the engine health monitoring (EHM) process that aims to detect, identify and predict engine component faults, using information from installed sensors, in order to guide maintenance action, maintain engine efficiency and prevent catastrophic failures. To achieve high prediction accuracies, current data-derived diagnostic models tend to be engine specific while the model-based methods are known to be time-consuming, especially for complex engine configurations. This paper proposes an integrated approach for accurate and accelerated isolation and prediction of multiple-degraded gas turbine component faults that comprises 3 steps — feature extraction using the Principal Component Analysis (PCA), machine learning classification with a multi-layer perceptron, artificial neural network (MLP-ANN) and model-based fault prediction via the non-linear Gas Path Analysis (GPA) technique. In this hybrid approach, the PCA first transforms the measurement fault signature into a fault-feature domain, which becomes an input to the multi-label ANN classifier used to isolate the potential faulty components. The non-linear GPA finally quantifies the magnitude of degradation that produced the recorded fault signature. Once trained and validated, the PCA-ANN model is deployed as part of the data processing mechanism prior to the actual GPA calculation. This method was assessed and validated using the thermodynamic performance model of a 2-shaft, high-bypass ratio, turbofan engine. For training and testing the PCA-ANN classifier, a total of 28,000 final samples for 14 measurement parameters, each averaged from 10 data points with Gaussian noise of zero mean and unit standard deviation, and implanted with single-, double- and triple-component fault cases of various magnitude, were generated by steady-state performance simulation of the engine model at its reference operating condition. Correlation analysis of this data set revealed the optimum sensor subset to be used for multi-component diagnostics. A quantitative analysis of the PCA-ANN fault isolation on the test set produced a classification accuracy of 96.6% and performed better on all metrics, compared to other multi-label classification algorithms. Finally, the proposed integrated approach achieved an average of 94.35% reduction in processing time, when compared to the conventional non-linear GPA by component-fault-cases (CFCs), while predicting implanted faults to the same accuracy.
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Arasy, Rahmat, and Basari. "Detection of hypertensive retinopathy using principal component analysis (PCA) and backpropagation neural network methods." In SECOND INTERNATIONAL CONFERENCE OF MATHEMATICS (SICME2019). Author(s), 2019. http://dx.doi.org/10.1063/1.5096735.

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Sen, Anupam. "Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.5.

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Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.
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