The Community for Technology Leaders
Green Image
Issue No. 05 - May (2012 vol. 34)
ISSN: 0162-8828
pp: 1031-1039
Shi Yu , Katholieke Universiteit Leuven, Leuven
Léon-Charles Tranchevent , Katholieke Universiteit Leuven, Leuven
Xinhai Liu , Katholieke Universiteit Leuven, Leuven
Wolfgang Glänzel , Katholieke Universiteit Leuven, Leuven
Johan A.K. Suykens , Katholieke Universiteit Leuven, Leuven
Bart De Moor , Katholieke Universiteit Leuven, Leuven
Yves Moreau , Katholieke Universiteit Leuven, Leuven
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
Clustering, data fusion, multiple kernel learning, Fisher discriminant analysis, least-squares support vector machine.
Shi Yu, Léon-Charles Tranchevent, Xinhai Liu, Wolfgang Glänzel, Johan A.K. Suykens, Bart De Moor, Yves Moreau, "Optimized Data Fusion for Kernel k-Means Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1031-1039, May 2012, doi:10.1109/TPAMI.2011.255
85 ms
(Ver 3.3 (11022016))