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Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors
Author(s) -
Fu GuangHui,
Cao DongSheng,
Xu QingSong,
Li HongDong,
Liang YiZeng
Publication year - 2011
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.1364
Subject(s) - pattern recognition (psychology) , support vector machine , artificial intelligence , kernel principal component analysis , kernel (algebra) , principal component analysis , feature vector , hyperplane , nonlinear system , categorical variable , generalization , margin (machine learning) , mathematics , computer science , kernel method , machine learning , mathematical analysis , physics , geometry , combinatorics , quantum mechanics
Abstract In this paper, a two‐step nonlinear classification algorithm is proposed to model the structure–activity relationship (SAR) between bioactivities and molecular descriptors of compounds, which consists of kernel principal component analysis (KPCA) and linear support vector machines (KPCA + LSVM). KPCA is used to remove some uninformative gradients such as noises and then exactly capture the latent structure of the training dataset using some new variables called the principal components in the kernel‐defined feature space. LSVM makes full use of the maximal margin hyperplane to give the best generalization performance in the KPCA‐transformed space. The combination of KPCA and LSVM can effectively improve the prediction performance compared with the linear SVM as well as two nonlinear methods. Three datasets related to different categorical bioactivities of compounds are used to evaluate the performance of KPCA + LSVM. The results show that our algorithm is competitive. Copyright © 2011 John Wiley & Sons, Ltd.