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» Clustering via Matrix Powering
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SDM
2009
SIAM
193views Data Mining» more  SDM 2009»
14 years 8 months ago
Agglomerative Mean-Shift Clustering via Query Set Compression.
Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets...
Xiaotong Yuan, Bao-Gang Hu, Ran He
CORR
2010
Springer
175views Education» more  CORR 2010»
13 years 11 months ago
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biolo...
Ryan Prescott Adams, George E. Dahl, Iain Murray
BMCBI
2006
149views more  BMCBI 2006»
13 years 10 months ago
HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteri
Background: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining s...
Roel G. W. Verhaak, Mathijs A. Sanders, Maarten A....
IJON
2008
173views more  IJON 2008»
13 years 10 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitabl...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
BMCBI
2006
170views more  BMCBI 2006»
13 years 10 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...