Optimized data fusion for kernel k-means clustering
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Shi Yu1, Léon-Charles Tranchevent, Xinhai Liu
1Knapp Center for Biomedical Discovery, Department of Medicine, Institute for Genomics and Systems Biology, University of Chicago, 900 E. 57th St. Room 10148, Chicago, IL 60637, USA.
Abstract:
This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from http://homes.esat.kuleuven.be/~sistawww/bio/syu/okkc.html.).
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