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Publication
197views
12 years 24 days ago
Convex non-negative matrix factorization for massive datasets
Non-negative matrix factorization (NMF) has become a standard tool in data mining, information retrieval, and signal processing. It is used to factorize a non-negative data matrix ...
C. Thurau, K. Kersting, M. Wahabzada, and C. Bauck...
CVPR
2010
IEEE
13 years 2 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
AUTOMATICA
2008
139views more  AUTOMATICA 2008»
13 years 5 months ago
Structured low-rank approximation and its applications
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equival...
Ivan Markovsky
IJCAI
2007
13 years 6 months ago
A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory informatio...
Dit-Yan Yeung, Hong Chang, Guang Dai
CDC
2009
IEEE
138views Control Systems» more  CDC 2009»
13 years 8 months ago
Semidefinite programming methods for system realization and identification
We describe semidefinite programming methods for system realization and identification. For each of these two applications, a variant of a simple subspace algorithm is presented, i...
Zhang Liu, Lieven Vandenberghe