title: Optimized data fusion for kernel k-means clustering.
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.).
authors.affiliation: Knapp 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.
authors.forename: Shi
authors.initials: S
authors.lastname: Yu
authors.forename: Léon-Charles
authors.initials: LC
authors.lastname: Tranchevent
authors.forename: Xinhai
authors.initials: X
authors.lastname: Liu
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Glänzel
authors.forename: Johan A K
authors.initials: JA
authors.lastname: Suykens
authors.forename: Bart
authors.initials: B
authors.lastname: De Moor
authors.forename: Yves
authors.initials: Y
authors.lastname: Moreau