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Constrained dual graph regularized orthogonal nonnegative matrix tri-factorization for co-clustering. (English) Zbl 1435.68259
Summary: Coclustering approaches for grouping data points and features have recently been receiving extensive attention. In this paper, we propose a constrained dual graph regularized orthogonal nonnegative matrix trifactorization (CDONMTF) algorithm to solve the coclustering problems. The new method improves the clustering performance obviously by employing hard constraints to retain the priori label information of samples, establishing two nearest neighbor graphs to encode the geometric structure of data manifold and feature manifold, and combining with biorthogonal constraints as well. In addition, we have also derived the iterative optimization scheme of CDONMTF and proved its convergence. Clustering experiments on 5 UCI machine-learning data sets and 7 image benchmark data sets show that the achievement of the proposed algorithm is superior to that of some existing clustering algorithms.
MSC:
68T05 Learning and adaptive systems in artificial intelligence
15A23 Factorization of matrices
62H30 Classification and discrimination; cluster analysis (statistical aspects)
68R10 Graph theory (including graph drawing) in computer science
Software:
COIL-20; JAFFE
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