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» Optimal Solutions for Sparse Principal Component Analysis
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CVPR
2012
IEEE
13 years 3 days ago
Submodular dictionary learning for sparse coding
A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: ent...
Zhuolin Jiang, Guangxiao Zhang, Larry S. Davis
CLASSIFICATION
2006
108views more  CLASSIFICATION 2006»
14 years 9 months ago
The Practice of Cluster Analysis
Abstracts "Mixtures at the Interface" David Scott, Rice University Mixture modeling provides an effective framework for complex, high-dimensional data. The potential of m...
Jon R. Kettenring
125
Voted
PAMI
2007
249views more  PAMI 2007»
14 years 9 months ago
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
— The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem...
Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Ma...
TCS
2008
14 years 9 months ago
Main-memory triangle computations for very large (sparse (power-law)) graphs
Finding, counting and/or listing triangles (three vertices with three edges) in massive graphs are natural fundamental problems, which received recently much attention because of ...
Matthieu Latapy
107
Voted
CVPR
2009
IEEE
15 years 28 days ago
Nonlinear Nonnegative Component Analysis
In this paper general solutions for Nonlinear Nonnegative Component Analysis for data representation and recognition are proposed. That is, motivated by a combination of the Nonne...
Stefanos Zafeiriou, Maria Petrou