429 search results - page 33 / 86 » Unsupervised feature selection for principal components anal... |
ICPR
2004
IEEE
14 years 8 months ago
2004
IEEE
Feature subset selection, applied as a pre-processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier perfo...
NIPS
1997
13 years 9 months ago
1997
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large col...
CANDC
2005
ACM
13 years 7 months ago
2005
ACM
Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ig...
IJON
2007
13 years 7 months ago
2007
Kernel principal component analysis (KPCA) extracts features of samples with an efficiency in inverse proportion to the size of the training sample set. In this paper, we develop...
ICCV
2003
IEEE
14 years 28 days ago
2003
IEEE
A new approach to automatically extract the main features in color fundus images are proposed in this paper. Optic disk is localized by the principal component analysis (PCA) and ...