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» Using Matrix Decompositions in Formal Concept Analysis
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SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
14 years 2 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
TPDS
2010
174views more  TPDS 2010»
13 years 6 months ago
Parallel Two-Sided Matrix Reduction to Band Bidiagonal Form on Multicore Architectures
The objective of this paper is to extend, in the context of multicore architectures, the concepts of tile algorithms [Buttari et al., 2007] for Cholesky, LU, QR factorizations to t...
Hatem Ltaief, Jakub Kurzak, Jack Dongarra
ICASSP
2011
IEEE
12 years 11 months ago
Co-clustering as multilinear decomposition with sparse latent factors
The K-means clustering problem seeks to partition the columns of a data matrix in subsets, such that columns in the same subset are ‘close’ to each other. The co-clustering pr...
Evangelos E. Papalexakis, Nicholas D. Sidiropoulos
GPB
2010
231views Solid Modeling» more  GPB 2010»
13 years 5 months ago
Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decompos
The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualiz...
Ferran Reverter, Esteban Vegas, Pedro Sánch...
CVPR
2004
IEEE
14 years 9 months ago
Linear Projection Methods in Face Recognition under Unconstrained Illuminations: A Comparative Study
Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. [2] presented a study on the comparison betwe...
Qi Li, Jieping Ye, Chandra Kambhamettu