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
Self-Explanatory Convex Sparse Representation for Image Classification
Bao-Di Liu,Yu-Xiong Wang,Bin Shen,Yu-Jin Zhang,Yanjiang Wang,Weifeng Liu +5 more
- 13 Oct 2013
TL;DR: By tracing back and connecting sparse representation with the K-means algorithm, a novel variation scheme termed as self-explanatory convex sparse representation (SCSR) has been proposed, where the basis vectors of the dictionary are refined as convex combination of the data points.
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Smart Diagnosis: A Multiple-Source Transfer TSK Fuzzy System for EEG Seizure Identification
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TL;DR: By inheriting the interpretability of TSK-FS, MST-TSK displays good interpretability in identifying EEG signals that are understandable by humans (domain experts).
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Learning with limited and noisy tagging
Yingming Li,Zhongang Qi,Zhongfei Zhang,Ming Yang +3 more
- 21 Oct 2013
TL;DR: A discriminative model is proposed, called SpSVM-MC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label constraints into the optimization of a robust tagging method.
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Latent Space Exploration Using Generative Kernel PCA
David Winant,Joachim Schreurs,Johan A. K. Suykens +2 more
- 06 Nov 2019
TL;DR: The use of generative kernel PCA for exploring latent spaces of datasets is investigated and the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored.
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