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
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Xuanzi Chen,Krzysztof Przystupa,Zhiwei Ye,Feng Chen,Chunzhi Wang,Jinhang Liu,Rong Gao,Ming Wei,Orest Kochan +8 more
TL;DR: A hybrid method, composed of kernel principal component analysis, tree seed algorithm based on Lévy flight and extreme learning machine (ELM), is proposed for short-term load forecasting, demonstrating the superiority of the proposed approaches compared to the other methods involved in the paper.
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TL;DR: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not, for teaching and research institutions in France or abroad, or from public or private research centers.
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Group Integration Techniques in Pattern Analysis - A Kernel View.
Marco Reisert
- 01 Jan 2008
TL;DR: This thesis assumes that the data does n t change its meaning under certain transformations, that is, the pattern recognition process has to be ’invariant’ under these transformations, and establishes a theoretical well-founded framework of invariance in pattern analysis.
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Cross-Project Software Defect Prediction Using Feature-Based Transfer Learning
He Qing,Li Biwen,Shen Beijun,Yong Xia +3 more
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TL;DR: The core insight of CPDP is to filter and transfer highly-correlated data based on data samples in the target projects, and evaluate and choose learning schemas for transferring data sets, which achieves similar f-measure and AUC as some inner-project defect prediction approaches.
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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