Relevant bibliographies by topics / Kernel component analysis (KPCA/KICA)

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

Academic literature on the topic 'Kernel component analysis (KPCA/KICA)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Kernel component analysis (KPCA/KICA)"

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Xu, Jie, Jin Zhao, Baoping Ma, and Shousong Hu. "Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/987345.

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New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA) and sparse support vector machine (SVM). The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA). The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE) chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.
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Wang, Tianzhen, Jingjing Dong, Tao Xie, Demba Diallo, and Mohamed Benbouzid. "A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion." Information 10, no. 3 (March 17, 2019): 116. http://dx.doi.org/10.3390/info10030116.

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This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%.
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Xiao, Ying Wang, and Ying Du. "Combination Method of Kernel Principal Component Analysis and Independent Component Analysis for Process Monitoring." Applied Mechanics and Materials 249-250 (December 2012): 153–58. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.153.

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A combination method of kernel principal component analysis (KPCA) and independent component analysis (ICA) for process monitoring is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
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Liu, Mingguang, Xiangshun Li, Chuyue Lou, and Jin Jiang. "A Fault Detection Method Based on CPSO-Improved KICA." Entropy 21, no. 7 (July 9, 2019): 668. http://dx.doi.org/10.3390/e21070668.

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In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR).
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Liu, Xiao Zhi, and Jing Li. "Multi-User Detection Based on Improved KICA with Bat Algorithm." Applied Mechanics and Materials 336-338 (July 2013): 1867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1867.

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In this paper, an improved kernel independent component analysis (KICA) algorithm is proposed for multi-user detection (MUD). In this algorithm, a new hybrid kernel function is adopted. In addition, the bat algorithm is applied to the optimizing process of independent component separation. Simulation results show that the new hybrid kernel function performs better in MUD than other kernel functions, and the improved KICA with bat algorithm has the smallest bit error rate (BER) when compared with classical FastICA and KICA algorithms.
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Bawotong, Vitawati, Hanny Komalig, and Nelson Nainggolan. "Plot Multivariate Menggunakan Kernel Principal Component Analysis (KPCA) dengan Fungsi Power Kernel." d'CARTESIAN 4, no. 1 (February 10, 2015): 95. http://dx.doi.org/10.35799/dc.4.1.2015.8106.

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Kernel PCA merupakan PCA yang diaplikasikan pada input data yang telah ditransformasikan ke feature space. Misalkan F: Rn®F fungsi yang memetakan semua input data xiÎRn, berlaku F(xi)ÎF. Salah satu dari banyak fungsi kernel adalah power kernel. Fungsi power kernel K(xi, xj) = –|| xi – xj ||b dengan 0 < b ≤ 1. Tujuan dari penelitian ini yaitu mempelajari penggunaan Kernel PCA (KPCA) dengan fungsi Power Kernel untuk membantu menyelesaikan masalah plot multivariate nonlinier terutama yang berhubungan dalam pengelompokan. Hasil menunjukkan bahwa Penggunaan KPCA dengan fungsi Power Kernel sangat membantu dalam menyelesaikan masalah plot multivariate yang belum dapat dikelompokan dengan garis pemisah yang linier. Kata kunci : Kernel Principal Component Analysis (KPCA), Plot Multivariate, Power Kernel
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Xie, Shengkun, Anna T. Lawniczak, Sridhar Krishnan, and Pietro Lio. "Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification." ISRN Computational Mathematics 2012 (July 29, 2012): 1–13. http://dx.doi.org/10.5402/2012/197352.

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We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.
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Wang, X. H., H. L. Mao, C. M. Zhu, and Z. F. Huang. "Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 2 (December 1, 2008): 525–29. http://dx.doi.org/10.1243/09544062jmes1296.

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A hydraulic turbine runner has a complex structure, and traditional source location methods do not have the higher accuracy to meet engineering requirements. The source location of crack acoustic emission (AE) signals in hydraulic turbine blades has been researched by combining it with kernel-independent component analysis (KICA) as feature extraction, with support vector machines (SVMs) as position recognition. This method is compared with those applied SVMs with feature extraction using kernel principal components analysis without feature extraction. The results show that the recognition rate in the crack region is 100 per cent by using both original AE parameters and feature parameters. Support vector regression by feature extraction using KICA can perform better than the other methods. As a result, it is a better method for source location of complex big size structures to combine KICA with SVM. It decreases the dimensionality of input signals and also improves the accuracy of location.
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Li, Zhi Chun. "New Detection Method for Gear Faults Based on Kernel Independent Component Analysis and BP Neural Network." Advanced Materials Research 909 (March 2014): 371–74. http://dx.doi.org/10.4028/www.scientific.net/amr.909.371.

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Gearboxes are widely used in various kinds of applications. The normal operation of the gears contributes important roles on the machine performance. Due to harsh environment the rolling bearings are prone to failures. Hence, it is essential to detect the gear faults. However, the vibration signals of the gearbox are often contaminated, leading to deterioration of the fault diagnosis performance. To address this issue, a new approach is proposed based on the kernel independent component analysis (KICA) and BP neural network (BPNN). The KICA was used to extract sensitive signals to eliminate noise signals. Then a BPNN was adopted to detect the gear fault. To improve the fault identification, the Genetic Algorithm (GA) was adopted to optimize the BP parameters. Experiment tests using the gearbox fault simulator have been implemented. The test results show that the noise signals have been eliminated by the KICA and the GA-BPNN can detect the gear fault accurately. Moreover, through comparison with other existing methods, the proposed KICA-GA-BPNN produced the best detection rate of 93.7%.
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Liu, Yi, Hong Ying Deng, Zeng Liang Gao, and Ping Li. "Soft Sensor Modeling via Support Vector Regression with KICA-Based Feature Extraction." Advanced Materials Research 186 (January 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amr.186.560.

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A novel two-level integrated soft sensor modeling method using kernel independent component analysis (KICA) and support vector regression (SVR) is proposed for chemical processes. In the first level, the KICA approach is adopted to extract information of input variables in the high dimensional feature space. Based on this strategy, the correlation of input variables can be eliminated and thus the complexity is reduced. Then, the model is established using SVR in the second level. The KICA-SVR soft sensor modeling method is applied to estimate product compositions in the Tennessee Eastman process. The obtained results show that it can exhibit better performance, compared to the traditional ICA, principal component analysis (PCA) and kernel PCA based information extraction methods, under different operating conditions.
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Dissertations / Theses on the topic "Kernel component analysis (KPCA/KICA)"

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Chung, Koon Yin C. "Facial Expression Recognition by Using Class Mean Gabor Responses with Kernel Principal Component Analysis." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1260468428.

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Vasilescu, M. Alex O. "A Multilinear (Tensor) Algebraic Framework for Computer Graphics, Computer Vision and Machine Learning." Thesis, 2012. http://hdl.handle.net/1807/65327.

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This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.
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Book chapters on the topic "Kernel component analysis (KPCA/KICA)"

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Li, Jun-Bao, Shu-Chuan Chu, and Jeng-Shyang Pan. "Kernel Principal Component Analysis (KPCA)-Based Face Recognition." In Kernel Learning Algorithms for Face Recognition, 71–99. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-0161-2_4.

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Islam, Seikh Mazharul, Minakshi Banerjee, and Siddhartha Bhattacharyya. "Dealing with Higher Dimensionality and Outliers in Content-Based Image Retrieval." In Advances in Data Mining and Database Management, 109–34. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1776-4.ch005.

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This chapter proposes a content based image retrieval method dealing with higher dimensional feature of images. The kernel principal component analysis (KPCA) is done on MPEG-7 Color Structure Descriptor (CSD) (64-bins) to compute low-dimensional nonlinear-subspace. Also the Partitioning Around Medoids (PAM) algorithm is used to squeeze search space again where the number of clusters are counted by optimum average silhouette width. To refine these clusters further, the outliers from query image's belonging cluster are excluded by Support Vector Clus-tering (SVC). Then One-Class Support Vector Machine (OCSVM) is used for the prediction of relevant images from query image's belonging cluster and the initial retrieval results based on the similarity measurement is feed to OCSVM for training. Images are ranked from the positively labeled images. This method gives more than 95% precision before recall reaches at 0.5 for conceptually meaningful query categories. Also comparative results are obtained from: 1) MPEG-7 CSD features directly and 2) other dimensionality reduction techniques.
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V. S., Bharath, Miraclin F., Bhanu Priyanka, Bharath K. P., and Rajesh Kumar M. "Investigation of Epileptic Seizures and Sleep Disturbance." In Advances in Medical Technologies and Clinical Practice, 52–70. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8018-9.ch004.

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In this chapter, the authors make use of signal processing techniques and machine learning models to analyze the EEG signal. First, the EEG signal is broken down into the frequency sub-bands using a discrete wavelet transform (DWT). Then the kernel principle component analysis (KPCA) method is used to reduce the dimension of data. They input these extracted features into a neural network to find if the patient has an epileptic seizure or not. The results of the classification process due to artificial neural networks (ANN) are studied and analyzed. Also, to recognize the abnormal activities in the EEG signal, caused by changes in neuronal electrochemical activity in epileptic patients, the EEG signal is processed using the Hilbert Huang transform (HHT). Given the wide array of epilepsy, we need to make use of intelligent devices in the treatment of epilepsy by using the patient's neurophysiology for better diagnosis before the clinical operation.
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Conference papers on the topic "Kernel component analysis (KPCA/KICA)"

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Ma, Jianping, and Jin Jiang. "Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29777.

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In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification in the instruments of nuclear power plants. We propose to use mean values of the sensor reconstruction errors of a KPCA model for fault isolation and identification. They provide useful information about the directions and magnitudes of detected faults, which are usually not available from other fault isolation techniques. The performance of the method is demonstrated by applications to real NPP measurements.
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Li, Liming, and Jing Zhao. "Comprehensive Evaluation of Parallel Mechanism and Robot Performance Based on Principal Component Analysis and Kernel Principal Component Analysis." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47032.

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Revealing the relations among parallel mechanism and robot comprehensive performance, topological structure and dimension is the basis to optimize mechanism. Due to the correlation and diversity of the single performance indexes, statistical principles of linear dimension reduction and nonlinear dimension reduction were introduced into comprehensive performance analysis and evaluation for typical parallel mechanisms and robots. Then the mechanism’s topological structure and dimension with the best comprehensive performance could be selected based on principal component analysis (PCA) and kernel principal component analysis (KPCA) respectively. Through comparing the results, KPCA could reveal the nonlinear relationship among different single performance indexes to provide more comprehensive performance evaluation information than PCA, and indicate the numerical calculation relations among comprehensive performance, topological structure and dimension validly.
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Koutsogiannis, Grigorios S., and John J. Soraghan. "Classification and de-noising of communication signals using kernel Principal Component Analysis (KPCA)." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5744942.

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Koutsogiannis and Soraghan. "Classification and de-noising of communication signals using kernel principal component analysis (KPCA)." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1006083.

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Liu, Yingtao, Masoud Yekani Fard, and Aditi Chattopadhyay. "Kernel Feature Space Based Low Velocity Impact Monitoring." In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-8242.

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Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient structural health monitoring methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this paper a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical feature matrices, composed of features extracted from the time and frequency domains, are developed. Kernel Principal Component Analysis (KPCA) is used to compress and classify the statistical feature matrices. Compared with traditional PCA algorithm, KPCA method shows better feature clustering and damage quantification capabilities. A new damage index, formulated using Mahalanobis distance, is defined to quantify impact damage. The developed methodology has been validated using low velocity impact experiments with a sandwich composite wing.
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Lu, Jun, Zhenfei Zhan, Pan Wang, Yudong Fang, and Junqi Yang. "A Stochastic Multivariate Validation Method for Dynamic Systems." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67690.

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As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges. One of the key difficulties for model validation is to evaluate the quality of a computer model with multivariate, highly correlated and non-normal data, the direct application of traditional validation approaches does not appear to be suitable. This paper proposes a stochastic method to validate the dynamic systems. Firstly, a dimension reduction utilizing kernel principal component analysis (KPCA) is used to improve the computational efficiency. A probability model is then established by non-parametric kernel density estimation (KDE) method, and differences between the test data and simulation results are finally extracted to further comparative validation. This new approach resolves some critical drawbacks of the previous methods and improves the processing ability to nonlinear problem to validation the dynamic model. The proposed method and process are successfully illustrated through a real-world vehicle dynamic system example. The results demonstrate that the method of incorporate with KPCA and KDE is an effective approach to solve the dynamic model validation problem.
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Liu, Hai, Yanyi Zhang, Dong Hao, Yong Chen, Xiang Ji, and Changyin Wei. "Objective Evaluation of FCV Interior Sound Quality During Acceleration." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87011.

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While driving a FCV during acceleration, many sorts of sounds could be heard, which influence the interior sound quality. A typical FCV is taken as a sample, four interior noises generated under the acceleration operation are collected in the whole vehicle semi-anechoic chamber, and the noise sample database of diesel engine radiation noise is established after preprocessing. Based on sound quality theory (physical and psychoacoustic features), the Kernel Principal Component Analysis (KPCA) is used to extract the key objective features mainly influencing the sound quality, which realize the dimension reduction target; the variations of objective features are analyzed to qualitatively analyze the law of the sound quality varying during acceleration. According to the objective evaluation of FCV interior sound quality, combining with FCV operating parameters, the influencing law of the FCV sound quality could be obtained.
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Chen, Chong, Shimin Zhang, Hang Zhang, Xiaojun Li, and Zichen He. "Research on Risk Assessment Method of Stick-Slip Vibration of the Bit Based on BP Neural Network Algorithm." In ASME 2018 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/pvp2018-84144.

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During the drilling process, the non-linear contacts between the bit and the bottom hole, the drill string and the borehole wall can cause the bit’s stick-slip vibration, which will shorten the life of the bit and even endanger the safety of the drill string. The severity of stick-slip vibration of a bit can be identified by the rotary speed of a bit, the triaxial accelerations of the drill string, the wellhead torque and other parameters measured by the measuring while drilling (MWD) tools in the downhole and devices on the surface. To evaluate the level of stick-slip vibration, this paper proposes a risk assessment method of sick-slip vibration based on backpropagation neural network (BPNN). According to the time and frequency domain analysis of the data collected from simulation, the feature parameters of the time and frequency domains of signals are extracted, and then the kernel principal component analysis (KPCA) is applied to reduce dimensions. Consequently, the feature vectors can be obtained, which become the input parameters of the BPNN. Based on BPNN algorithm, the stick-slip vibration of the bit is determined, and the classification of stick-slip vibration strength is carried out. The results show that this method can effectively identify the severity of stick-slip vibration of a bit. Therefore, this method is valid to evaluate the stick-slip vibration of a bit, which will help drillers adjust the drilling parameters practically according to the severity of vibration, so as to reduce the risks of stick-slip vibration during drilling and improve the efficiency and safety of drilling operation.
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