ArticleBioinformatics (Oxford, England)2011
Optimized data fusion for K-means Laplacian clustering.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A Survey on Multi-View Clustering.IEEE transactions on artificial intelligence · 2021Article
- Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.BMC medical imaging · 2017Article
- Finding reproducible cluster partitions for the k-means algorithm.BMC bioinformatics · 2013Article
- Functional Module Analysis for Gene Coexpression Networks with Network Integration.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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7 authors.
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Abstract
motivationWe 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.
resultsThree 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. AVAILABILITY: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html.
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