CORR
2007
Springer
13 years 11 months ago
2007
Springer
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
CORR
2007
Springer
13 years 11 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...
NIPS
2008
14 years 26 days ago
2008
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as spec...
ICML
2007
IEEE
15 years 7 days ago
2007
IEEE
Eigenvalue problems are rampant in machine learning and statistics and appear in the context of classification, dimensionality reduction, etc. In this paper, we consider a cardina...