502 search results - page 10 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
SDM
2010
SIAM
13 years 6 months ago
2010
SIAM
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the o...
NIPS
2003
13 years 9 months ago
2003
Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projecti...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
ICCS
2003
Springer
14 years 22 days ago
2003
Springer
Sparse Grids are the basis for efficient high dimensional approximation and have recently been applied successfully to predictive modelling. They are spanned by a collection of si...
IEEEMM
2007
13 years 7 months ago
2007
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...