Optimized Data Fusion for Kernel k-Means Clustering

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 p...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-05, Vol.34 (5), p.1031-1039
Main Authors: Shi Yu, Tranchevent, Leon-Charles, Xinhai Liu, Glanzel, W., Suykens, J. A. K., De Moor, B., Moreau, Y.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Clustering
Clustering algorithms
Complexity theory
Computer science
control theory
systems
data fusion
Data processing. List processing. Character string processing
Exact sciences and technology
Fisher discriminant analysis
Hilbert space
Kernel
Learning and adaptive systems
least-squares support vector machine
Memory organisation. Data processing
multiple kernel learning
Partitioning algorithms
Software
Support vector machines
Theoretical computing
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Summary: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.).
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2011.255