Latent Wishart Processes for Relational Kernel Learning




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Latent Wishart Processes for Relational Kernel Learning Wu Jun Li Zhihua Zhang Dit Yan Yeung Dept of Comp Sci and Eng College of Comp Sci and Tech Dept of Comp Sci and Eng Hong Kong Univ of Sci and Tech Zhejiang University Hong Kong Univ of Sci and Tech Hong Kong China Zhejiang 310027 China Hong Kong China liwujun cse ust hk zhzhang cs zju edu cn dyyeung cse ust hk Abstract One main concern towards kernel classifiers is on their sensitivity to the choice of kernel function or kernel matrix which characterizes the similarity between instances Many realworld data such as web pages and proteinprotein interaction data are relational in nature in the sense that different instances are correlated linked with each other The relational information available in such data often provides strong hints on the correlation or similarity between instances In this paper we propose a novel relational kernel learning model based on latent Wishart processes LWP to learn the kernel function for relational data This is done by seamlessly integrating the relational information and the input attributes into the kernel learning process Through extensive experiments on realworld applications we demonstrate that our LWP model can give very promising performance in practice 1 Introduction Kernel methods such as support vector machines SVM and Gaussian processes GP Rasmussen and Williams 2006 have been widely used in many applications giving very promising performance In kernel methods the similarity between instances is represented by a kernel function defined over the input attributes In general the choice of an appropriate kernel function and its corresponding parameters is difficult in practice Poorly chosen kernel functions Appearing in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics AISTATS 2009 Clearwater Beach Florida USA Volume 5 of JMLR W CP 5 Copyright 2009 by the authors can impair the performance significantly Hence kernel learning






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