287 search results - page 18 / 58 » Optimal Solutions for Sparse Principal Component Analysis |
92
Voted
NIPS
2003
14 years 11 months ago
2003
In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K−means method. We have proved ...
82
Voted
ICDM
2007
IEEE
15 years 1 months ago
2007
IEEE
Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, informati...
ECCV
2002
Springer
15 years 11 months ago
2002
Springer
Principal ComponentAnalysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the var...
KDD
2001
ACM
15 years 10 months ago
2001
ACM
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
CDC
2009
IEEE
15 years 1 months ago
2009
IEEE
The conventional independent component regression (ICR), as an exclusive two-step implementation algorithm, has the risk similar to principal component regression (PCR). That is, t...