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About kernel latent variable approaches and SVM
Author(s) -
Czekaj Tomasz,
Wu Wen,
Walczak Beata
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.937
Subject(s) - latent variable , kernel (algebra) , support vector machine , kernel regression , artificial intelligence , computer science , latent variable model , kernel method , machine learning , principal component regression , pattern recognition (psychology) , mathematics , regression , regression analysis , statistics , combinatorics
The aim of this paper is to demonstrate, that kernel latent variables approaches have a comparable predictive power with the set of kernel approaches based on regularization (e.g. Support Vector Machines). Kernel latent variable approaches are an alternative to kernel ridge regression, in the same way as PCR or PLS are the alternative approaches to Ridge Regression. Performance of these approaches is demonstrated for simulated data sets and microarray data set. Copyright © 2006 John Wiley & Sons, Ltd.