502 search results - page 17 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
NIPS
2008
13 years 9 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
NIPS
2008
13 years 9 months ago
2008
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
BMCBI
2006
13 years 7 months ago
2006
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...