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
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Improving probabilistic hazard forecasts in volcanic fields. Application to Harrat Rahat, Kingdom of Saudi Arabia
Melody G. Runge
- 01 Jan 2015
TL;DR: In this article, the authors used an anisotropic kernel spatial smoothing method with an expanding volcanic field boundary to forecast future volcanic activity in the Kingdom of Saudi Arabia (Harrat Rahat).
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•Proceedings Article
Feature and kernel learning.
Verónica Bolón-Canedo,Michele Donini,Fabio Aiolli +2 more
- 01 Jan 2015
TL;DR: A survey of recent methods developed for feature selection/learning and their application to real world problems is provided, together with a review of the contributions to the ESANN 2015 special session on Feature and Kernel Learning.
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Sparse KPCA for feature extraction in speech recognition
Amaro A. de Lima,Heiga Zen,Yoshihiko Nankaku,Keiichi Tokuda,Tadashi Kitamura,F.G.V. Resende +5 more
- 18 Mar 2005
TL;DR: The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.
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A lightweight 3D-2D convolutional neural network for spectral-spatial classification of hyperspectral images
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•Dissertation
Non-linear and sparse representations for multi-modal recognition
Hien M. Nguyen
- 01 Jan 2013
TL;DR: This dissertation addresses the problem of representing 2D and 3D shapes and introduces a novel implicit shape representation based on Support Vector Machine (SVM) theory, and presents dictionary learning methods for sparse and redundant representations in a high-dimensional feature space.
8
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