Relevant bibliographies by topics / Feature based Principal Component Analysis (FPCA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Feature based Principal Component Analysis (FPCA)'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Feature based Principal Component Analysis (FPCA)"

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Salvatore, Stefania, Jørgen G. Bramness, and Jo Røislien. "Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data." BMC Medical Research Methodology 16, no. 1 (2016): 81. https://doi.org/10.1186/s12874-016-0179-2.

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<strong>Background: </strong>Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally.<strong>Methods: </strong>We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal feature
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Pesaresi, Simone, Adriano Mancini, Giacomo Quattrini, and Simona Casavecchia. "Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series." Remote Sensing 12, no. 7 (2020): 1132. http://dx.doi.org/10.3390/rs12071132.

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The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenolog
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K, N. Kusuma, and Lakshmi Ram Prasath H. "Application of Feature Based Principal Component Analysis (FPCA) technique on Landsat8 OLI multispectral data to map Kimberlite pipes." Indian Journal of Science and Technology 14, no. 4 (2021): 361–72. https://doi.org/10.17485/IJST/v14i4.1741.

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Abstract <strong>Objectives:</strong>&nbsp;To map the kimberlite pipes emplaced in parts of Anantpur District, India using Landsat-8 OLI multispectral data. Kimberlite are considered as the primary host of natural diamond. Kimberlite pipes have very limited exposure and are altered, therefore the indirect surface indicators associated with kimberlite such as ferric iron bearing minerals (hematite, goethite), hydroxyl (clay) and carbonate (calcrete) minerals, were mapped to trace kimberlite pipe.&nbsp;<strong>Methods:</strong>&nbsp;Feature based Principal Component Analysis (FPCA) was applied o
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Montanino, Andrea, Gianluca Alaimo, and Ettore Lanzarone. "A gradient-based optimization method with functional principal component analysis for efficient structural topology optimization." Structural and Multidisciplinary Optimization 64, no. 1 (2021): 177–88. http://dx.doi.org/10.1007/s00158-021-02872-9.

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AbstractStructural topology optimization (STO) is usually treated as a constrained minimization problem, which is iteratively addressed by solving the equilibrium equations for the problem under consideration. To reduce the computational effort, several reduced basis approaches that solve the equilibrium equations in a reduced space have been proposed. In this work, we apply functional principal component analysis (FPCA) to generate the reduced basis, and we couple FPCA with a gradient-based optimization method for the first time in the literature. The proposed algorithm has been tested on a l
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Hael, Mohanned A., Hai Qiang Ma, Hamas A. AL-kuhali, and Zeinab Rizk. "Quantile-based Clustering for Functional Data via Modelling Functional Principal Components Scores." Journal of Physics: Conference Series 2449, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2449/1/012016.

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Abstract Clustering tasks of functional data arise naturally in many applications, and efficient classification approaches are needed to find groups. The current paper combines the quantile-based model with the principal component analysis of functional data (FPCA). In our proposed procedures, the projection of functional data is first approximated based on (rotated) FPCA. The quantile-based model is then implemented on the space of rotated scores to identify the potential features of underlying clusters. The proposed method overcomes the limitation of using direct basis function expansion suc
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P., Gopinath. "Raspberry PI-Based Finger Vein Recognition System Using PCA NET." International Research Journal of Computer Science 10, no. 06 (2023): 414–18. http://dx.doi.org/10.26562/irjcs.2023.v1006.24.

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Despite simultaneously ignoring the intensity distribution that is formed by the finger tissue and, in some instances, processing it as background noise, the majority of finger vein feature extraction algorithms achieve satisfactory performance due to their ability to represent texture. This project makes use of two- directional two-dimensional Fisher Principal Component Analysis, also known as (2D) 2 FPCA, for feature extraction. This type of "noise" is presented as a novel soft biometric trait for improving finger vein recognition performance. In order to demonstrate that the intensity distr
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Kusuma, K. N. "Application of Feature Based Principal Component Analysis (FPCA) technique on Landsat8 OLI multispectral data to map Kimberlite pipes." Indian Journal of Science and Technology 14, no. 4 (2021): 361–72. http://dx.doi.org/10.17485/ijst/v14i4.1741.

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Zhou, Xin, and Xianqing Lei. "Fault Diagnosis Method of the Construction Machinery Hydraulic System Based on Artificial Intelligence Dynamic Monitoring." Mobile Information Systems 2021 (July 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/1093960.

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This paper aims to study the fault diagnosis method of the mechanical hydraulic system based on artificial intelligence dynamic monitoring. According to the characteristics of functional principal component analysis (FPCA) and neural network in the fault diagnosis method in the feature extraction process, the fault diagnosis method combining functional principal component analysis and BP neural network is studied and it is applied to the fault of the coordinator hydraulic system diagnosis. This article mainly completed the following tasks: analyzing the structure and working principle of the m
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Hou, Yunhui, Na Shen, and Yubin Lin. "A Classification Method for Multichannel MI-EEG Signal with FPCA and DNN." Journal of Physics: Conference Series 2891, no. 11 (2024): 112014. https://doi.org/10.1088/1742-6596/2891/11/112014.

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Abstract A new accurate identification method has been proposed to address the lack of interpretability in current deep learning-based feature extraction methods for motor imagery electroencephalogram (MI-EEG) signals. This method combines functional principal component analysis (FPCA) and deep neural networks (DNN) for four classifications of MI-EEG signals. The process involves preprocessing the acquired MI-EEG signals and obtaining power spectral density (PSD) versus frequency curves in the alpha band for multiple channels and samples through FIR filtering. All PSD-frequency curves are then
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B D, Mr Darshan, Vyshnavi Shekhar B S, Meghana M. Totiger, Priyanka N, and Spurthi A. "Classification of Emotion using Eeg Signals: an FPGA Based Implementation." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 102–9. http://dx.doi.org/10.35940/ijrte.b7808.0712223.

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An electroencephalograph is a device that records all electrical energy in the human brain using wearable metal electrodes placed on the skull. Electrical impulses connect brain cells and are always mobile, even at rest. This activity appears as a squiggly line in EEG recordings. Activity gaze data is pre-processed to a frequency range of 0 to 75 Hz. This creates a new matrix with a sample rate of 200 Hz and a range of 0-75 Hz. A finite-impulse-response low-pass filter was used because the bandpass would distort his EEG data after processing. Each pre-processed EEG signal has an output, which
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Dissertations / Theses on the topic "Feature based Principal Component Analysis (FPCA)"

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Li, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/3656.

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This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combin
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Li, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis." University of Sydney, 2008. http://hdl.handle.net/2123/3656.

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Master of Engineering<br>This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer
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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.
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Bird, Gregory David. "Linear and Nonlinear Dimensionality-Reduction-Based Surrogate Models for Real-Time Design Space Exploration of Structural Responses." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8653.

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Design space exploration (DSE) is a tool used to evaluate and compare designs as part of the design selection process. While evaluating every possible design in a design space is infeasible, understanding design behavior and response throughout the design space may be accomplished by evaluating a subset of designs and interpolating between them using surrogate models. Surrogate modeling is a technique that uses low-cost calculations to approximate the outcome of more computationally expensive calculations or analyses, such as finite element analysis (FEA). While surrogates make quick predictio
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Ersoy, Mehmet Okan. "Application Of A Natural-resonance Based Feature Extraction Technique To Small-scale Aircraft Modeled By Conducting Wires For Electromagnetic Target Classification." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605522/index.pdf.

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The problem studied in this thesis, is the classification of the small-scale aircraft targets by using a natural resonance based electromagnetic feature extraction technique. The aircraft targets are modeled by perfectly conducting, thin wire structures. The electromagnetic back-scattered data used in the classification process, are numerically generated for five aircraft models. A contemporary signal processing tool, the Wigner-Ville distribution is employed in this study in addition to using the principal components analysis technique to extract target features mainly from late-time target r
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Trahan, Patrick. "Classification of Carpiodes Using Fourier Descriptors: A Content Based Image Retrieval Approach." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1085.

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Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content bas
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Chen, Beichen, and Amy Jinxin Chen. "PCA based dimensionality reduction of MRI images for training support vector machine to aid diagnosis of bipolar disorder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259621.

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This study aims to investigate how dimensionality reduction of neuroimaging data prior to training support vector machines (SVMs) affects the classification accuracy of bipolar disorder. This study uses principal component analysis (PCA) for dimensionality reduction. An open source data set of 19 bipolar and 31 control structural magnetic resonance imaging (sMRI) samples was used, part of the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study funded by the NIH Roadmap Initiative aiming to foster breakthroughs in the development of novel treatments for neuropsychiatric disorders. The ima
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Bie, Yifeng. "Functional principal component analysis based machine learning algorithms for spectral analysis." Thesis, 2021. http://hdl.handle.net/1828/13372.

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The ability to probe molecular electronic and vibrational structures gives rise to optical absorption spectroscopy, which is a credible tool used in molecular quantification and classification with high sensitivity, low limit of detection (LoD), and immunity to electromagnetic noises. Spectra are sensitive to slight analyte variations, so they are often used to identify a sample’s components. This thesis proposes several methods for quick classification and quantification of analysts based on their absorbance spectra. functional Principal Component Analysis (fPCA) is employed for feature
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Le, Duc Huy, and 黎德輝. "Comparing principal component analysis and similarity feature-based selection and classification algorithms for face recognition." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/839cpa.

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碩士<br>國立高雄應用科技大學<br>製造與管理外國學生碩士專班<br>102<br>Abstract Purpose: Nowadays, face recognition has many applications in the real world. It has attracted a lot of attention of researchers. They have divided face recognition system involves three main steps: face detection, feature extraction and face classification. Feature extraction and feature selection are two important steps in facial image recognition problems. The objective of them is to reduce classification errors. Finding an efficient algorithm for these steps is a challenging task. In this research, we compared two methods based on global
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Books on the topic "Feature based Principal Component Analysis (FPCA)"

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretic
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Book chapters on the topic "Feature based Principal Component Analysis (FPCA)"

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Ananda, Ridho, Dina Rachmawaty, Budi Pratikno, Odai Amer Hamid, and Maifuza Binti Mohd Amin. "Improved Clustering-Based Feature Selection Using Feature Extraction Based on Principal Component Analysis." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81065-7_2.

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Yao, Biyuan, Jianhua Yin, Hui Li, Hui Zhou, and Wei Wu. "Channel Feature Extraction and Modeling Based on Principal Component Analysis." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1026-3_15.

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Taguchi, Y.-h. "Principal Component Analysis-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis." In Intelligent Computing Theories and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_90.

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Ibrahim, Abdelhameed, Aboul Ella Hassanien, and Siddhartha Bhattacharyya. "3D Object Recognition Based on Data Fusion at Feature Level via Principal Component Analysis." In Recent Trends in Signal and Image Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8863-6_18.

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Wu, Yun, Qiang Wang, and Yu Shi. "Research on Principal Component Feature Extraction Method Based on Improved Pearson Correlation Coefficient Analysis." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_11.

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Bhavani, D., N. Madhavi, V. Sireesha, M. Swetha, and P. Kishore Kumar. "Principal Component Analysis-Based Pre-Trained Neural Network for Liver Cancer Data Feature Extraction." In Recent Developments in Microbiology, Biotechnology and Pharmaceutical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003618140-171.

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Taguchi, Y. H. "Sincle Cell RNA-seq Analysis Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction." In Studies in Big Data. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9158-4_11.

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Taguchi, Y. H. "Multiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction." In Studies in Big Data. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9158-4_1.

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Chen, Chunjun, and Linshuying Huang. "Construction of Bearing Performance Degradation Indicators for Adaptive Improvement of Principal Component Analysis." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7887-4_101.

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Abstract Rolling bearing health assessment relies on the constructed degradation indicators, In order to improve the monotonicity and trend of the indicators, a fusion indicator construction method considering feature burr removal is proposed. For the feature burrs appearing in the rolling bearing degradation performance characterization features that deviate from the expected degradation trend, a criterion-based adaptive burr removal strategy is used to detect and remove the burrs existing in the characterization features to improve the performance of the degradation indicators; then, principal component analysis (PCA) is used to fuse six kinds of time domain features, to remove the redundant information in the original state feature space, to maintain the global structure of the bearing degradation data, and further use the Exponentially Weighted Moving Average (EWMA) algorithm to smooth the fusion indicators to obtain high-quality degradation indicators. The experimental results verify the superiority of the proposed method in terms of monotonicity and trend.
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Li, Jia wei, and Ming Sun. "A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis." In Computer and Computing Technologies in Agriculture IV. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18369-0_5.

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Conference papers on the topic "Feature based Principal Component Analysis (FPCA)"

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Routh, Bikky, Vikram Kumawat, Arijit Guha, Siddhartha Mukhopadhyay, and Amit Patra. "State-of-Health Estimation of Li-ion Batteries using Multiple Linear Regression and Optimized Feature Extraction based on Principal Component Analysis." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10626868.

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Liu, Heyuan, Yi Zhao, and Fran�ois Mar�chal. "On the role of artificial intelligence in feature oriented multi-criteria decision analysis." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.175488.

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Balancing economic and environmental goals in industrial applications is critical amid challenges like climate change. Multi-objective optimization (MOO) and multi-criteria decision analysis (MCDA) are key tools for addressing conflicting objectives. MOO generates viable solutions, while MCDA selects the optimal option based on key performance indicators such as profitability, environmental impact, safety, and efficiency. However, large datasets pose a challenge in selecting the preferred solution during the MCDA process This study introduces a novel machine learning-enhanced MCDA framework an
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Jun-Ling Xu, Bao-Wen Xu, Wei-Feng Zhang, and Zi-Feng Cui. "Principal Component Analysis based Feature Selection for clustering." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620449.

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Li, Zhangyu, and Yihui Qiu. "Feature selection based on improved principal component analysis." In CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning. ACM, 2023. http://dx.doi.org/10.1145/3590003.3590036.

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Yaicharoen, Auapong, Kotaro Hashikura, Md Abdus Samad Kamal, and Kou Yamada. "Principal Component Analysis-based Customizable Feature Selection Algorithm." In 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2022. http://dx.doi.org/10.1109/ecti-con54298.2022.9795390.

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Huibo, Zhao, Pan Quan, and Cheng Yongmei. "Feature Extraction Based on Mixture Probabilistic Kernel Principal Component Analysis." In 2009 International Forum on Information Technology and Applications (IFITA). IEEE, 2009. http://dx.doi.org/10.1109/ifita.2009.11.

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Hasan, Hasmarina, and Nooritawati Md Tahir. "Feature selection of breast cancer based on Principal Component Analysis." In its Applications (CSPA). IEEE, 2010. http://dx.doi.org/10.1109/cspa.2010.5545298.

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Lhazmir, Safae, Ismail El Moudden, and Abdellatif Kobbane. "Feature extraction based on principal component analysis for text categorization." In 2017 International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN). IEEE, 2017. http://dx.doi.org/10.23919/pemwn.2017.8308030.

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Gulati, Vineeta, Neeraj Raheja, and Rajneesh Kumar Gujral. "Pica-A Hybrid Feature Extraction Technique Based on Principal Component Analysis and Independent Component Analysis." In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022. http://dx.doi.org/10.1109/gcat55367.2022.9971838.

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Yu, REN, HUI Ji-zhuang, SHI Ze, ZHANG Ze-yu, Zhang Xu-hui, and Fan Hong-wei. "Feature Extraction of Loader Operation Based on Kernel Principal Component Analysis." In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2021. http://dx.doi.org/10.1109/icsp51882.2021.9408684.

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