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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Bilinear spatiotemporal basis models
TL;DR: The bilinear model is applied to natural spatiotemporal phenomena, including face, body, and cloth motion data, and compared in terms of compaction, generalization ability, predictive precision, and efficiency to existing models.
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
TL;DR: The emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatic, public health informatics and medical signal analytics is presented.
D3M: A Deep Domain Decomposition Method for Partial Differential Equations
TL;DR: In this article, a deep domain decomposition method based on the variational principle is proposed for partial differential equations (PDEs), which can be formulated as the solution of a constrained optimization problem, and a hierarchical neural network framework is designed to solve this optimization problem.
Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
TL;DR: It is shown the driving color map based on DSAE facilitates better visualization of driving behavior, by mapping the extracted 3-D hidden feature to the red green blue (RGB) color space.
155
Kernel Slow Feature Analysis for Scene Change Detection
Chen Wu,Liangpei Zhang,Bo Du +2 more
TL;DR: A novel scene change detection method via kernel slow feature analysis (KSFA) and postclassification fusion, which integrates independent scene classification with scene change Detection to accurately determine scene changes and identify the “from-to” transition type.
155
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