502 search results - page 2 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
IDEAL
2005
Springer
13 years 9 months ago
2005
Springer
Abstract. Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular es...
NIPS
2003
13 years 5 months ago
2003
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...
APWEB
2006
Springer
13 years 8 months ago
2006
Springer
Clustering is to identify densely populated subgroups in data, while correlation analysis is to find the dependency between the attributes of the data set. In this paper, we combin...
CSB
2003
IEEE
13 years 9 months ago
2003
IEEE
The problem of clustering continuous valued data has been well studied in literature. Its application to microarray analysis relies on such algorithms as -means, dimensionality re...
CORR
2007
Springer
13 years 4 months ago
2007
Springer
In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combi...