Book Chapter10.1007/BFB0020217
Kernel Principal Component Analysis
Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller +2 more
- 08 Oct 1997
- pp 583-588
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TL;DR: A new method for performing a nonlinear form of Principal Component Analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
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Abstract: A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
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Citations
Contour Structural Profiles: An Edge-Aware Feature Extractor for Hyperspectral Image Classification
TL;DR: In this article , an edge-aware feature extractor called contour structural profiles (CSPs) is proposed to extract the discriminative features for hyperspectral images classification.
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Neural network based intrusion detection using Bayesian with PCA and KPCA feature extraction
Tareek M. Pattewar,Harshal A. Sonawane +1 more
- 01 Nov 2015
TL;DR: Two techniques of intrusion detection system are presented, using Bayesian for noise reduction and kernel principal component analysis (KPCA) for feature extraction, which are promising for giving good performance results.
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Symmetry Based Two-Dimensional Principal Component Analysis for Face Recognition
Mingyong Ding,Congde Lu,Yunsong Lin,Ling Tong +3 more
- 03 Jun 2007
TL;DR: A symmetry based two-dimensional principal component analysis (S2DPCA) is presented, which combines the advantages of 2D PCA and of the SPCA and the experimental results show that S2 DPCA is competitive with or superior to 2DPC a and SPCa.
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Learning kernel subspace for face recognition
Jianwu Li,Wangli Hao,Xiao Zhang +2 more
TL;DR: In this article, a radial basis function neural network (RBFNN) was proposed to learn the feature extraction process of kernel subspace methods, specifically, Kernel Principle Component Analysis (KPCA) and two-phase Kernel Linear Discriminant Analysis (LDA), in order to improve the running efficiency of testing phase of kernel-based face recognition system.
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The kernel trick for nonlinear factor modeling
TL;DR: This study estimates factors nonlinearly through the kernel method, which allows for flexible nonlinearities while still avoiding the curse of dimensionality, and demonstrates that this approach can offer substantial advantages over mainstream methods.
9
References
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
A training algorithm for optimal margin classifiers
Bernhard E. Boser,Isabelle Guyon,Vladimir Vapnik +2 more
- 01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Application of the Karhunen-Loeve procedure for the characterization of human faces
Michael Kirby,Lawrence Sirovich +1 more
TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
2.8K
Simplified neuron model as a principal component analyzer
TL;DR: A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived.
2.6K