Optimized data fusion for K-means Laplacian clustering

Shi Yu, Xinhai Liu, Léon Charles Tranchevent, Wolfgang Glänzel, Johan A.K. Suykens, Bart de Moor, Yves Moreau

Research output: Contribution to journalArticle

13 Citations (Scopus)


Motivation: 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.

Original languageEnglish
Article numberbtq569
Pages (from-to)118-126
Number of pages9
Issue number1
Publication statusPublished - Jan 1 2011


ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Yu, S., Liu, X., Tranchevent, L. C., Glänzel, W., Suykens, J. A. K., de Moor, B., & Moreau, Y. (2011). Optimized data fusion for K-means Laplacian clustering. Bioinformatics, 27(1), 118-126. [btq569]. https://doi.org/10.1093/bioinformatics/btq569