Indefinite kernel ridge regression and its application on QSAR modelling

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Pages (from-to)127-133
Journal / PublicationNeurocomputing
Volume158
Publication statusPublished - 22 Jun 2015

Abstract

Recently, the use of indefinite kernels in machine learning has attracted numerous attentions. However most works are focused on the classification techniques and less are devoted to regression models. In this paper to adapt indefinite kernels to ridge regression model, an indefinite kernel ridge regression model is proposed. Instead of performing spectral transformation on the kernel matrix, a less restrictive semi-definite proxy kernel can be constructed to approximate the kernel which normally is positive semi-definite. The sensitivity of the distance between this indefinite kernel and the proxy kernel is controlled by a parameter ρ.This approach allows one to construct regression models of response values based on the similarities of corresponding objects, where the requirement on similarity measures to satisfy Mercers condition can be relaxed. To illustrate the use of this algorithm, it was applied to the quantitative structure-activity relationship (QSAR) modelling over 16 drug targets.

Research Area(s)

  • Computer aided drug design, Indefinite kernel, Quantitative structure-activity relationship (QSAR) modelling, Regression analysis

Citation Format(s)

Indefinite kernel ridge regression and its application on QSAR modelling. / Li, Benjamin Yee Shing; Yeung, Lam Fat; Ko, King Tim.

In: Neurocomputing, Vol. 158, 22.06.2015, p. 127-133.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal