502 search results - page 3 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
VLDB
2000
ACM
13 years 8 months ago
2000
ACM
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...
JMLR
2010
12 years 11 months ago
2010
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
KDD
2001
ACM
14 years 5 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,...
BMCBI
2010
13 years 4 months ago
2010
Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
ICCV
2001
IEEE
14 years 6 months ago
2001
IEEE
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares...