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TKDE
2010
224views more  TKDE 2010»
13 years 1 months ago
Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
Coclustering heterogeneous data has attracted extensive attention recently due to its high impact on various important applications, such us text mining, image retrieval, and bioin...
Yanhua Chen, Lijun Wang, Ming Dong
SDM
2008
SIAM
168views Data Mining» more  SDM 2008»
13 years 7 months ago
Semi-Supervised Clustering via Matrix Factorization
The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. U...
Fei Wang, Tao Li, Changshui Zhang
ICDM
2007
IEEE
149views Data Mining» more  ICDM 2007»
14 years 21 days ago
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
Tao Li, Chris H. Q. Ding, Michael I. Jordan
BMCBI
2006
170views more  BMCBI 2006»
13 years 6 months ago
Biclustering of gene expression data by non-smooth non-negative matrix factorization
Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of gene...
Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Fra...