Book Chapter10.1007/BFB0020217
Kernel Principal Component Analysis
Bernhard Schölkopf,Alexander J. Smola,Klaus-Robert Müller +2 more
- 08 Oct 1997
- pp 583-588
2.6K
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
A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction
Brice Hoffmann,Brice Hoffmann,Brice Hoffmann,Mikhail Zaslavskiy,Jean-Philippe Vert,Jean-Philippe Vert,Jean-Philippe Vert,Véronique Stoven,Véronique Stoven,Véronique Stoven +9 more
TL;DR: This study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets and provides a new benchmark for future work in this field, as well as discussing two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction.
WGAN Domain Adaptation for EEG-Based Emotion Recognition
Yun Luo,Si-Yang Zhang,Wei-Long Zheng,Bao-Liang Lu +3 more
- 13 Dec 2018
TL;DR: The experimental results demonstrate that the WGANDA framework successfully handles the domain shift problem in cross-subject EEG-based emotion recognition and significantly outperforms the state-of-the-art domain adaptation methods.
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Deep Survival Machines : Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks
TL;DR: In this article, a fully parametric estimation of survival times with competing risks in the presence of censoring is proposed, which does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Coxproportional hazard model.
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Kernel pricipal component analysis and the construction of non-linear active shape models
Carole J. Twining,Christopher J. Taylor +1 more
- 01 Jan 2001
TL;DR: It is shown that using such a model to impose shape constraints during Active Shape Model (ASM) search gives improved segmentations of worm images than those obtained using linear shape constraints.
Robust statistical approaches for local planar surface fitting in 3D laser scanning data
TL;DR: The proposed robust methods, called DetRD-PCA and DetRPCA, are significantly more efficient, faster, and produce more accurate fits and robust local statistics, necessary for many point cloud processing tasks.
94
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.
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