Optimized data fusion for K-means Laplacian clustering.
BACKGROUND
We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically.


RESULTS
Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix.


BACKGROUND
The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html.
Yu
Shi
S
Signals, Identification, System Theory and Automation, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven-Heverlee, Belgium. shiyu@uchicago.edu
Liu
Xinhai
X
Tranchevent
Léon-Charles
LC
Glänzel
Wolfgang
W
Suykens
Johan A K
JA
De Moor
Bart
B
Moreau
Yves
Y
D000465:Algorithms; D016000:Cluster Analysis; D004194:Disease / Q000235:genetics; D012984:Software