502 search results - page 4 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
JMLR
2010
13 years 1 days ago
2010
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
JMLR
2012
11 years 7 months ago
2012
Traditional tensor decompositions such as the CANDECOMP / PARAFAC (CP) and Tucker decompositions yield higher-order principal components that have been used to understand tensor d...
NIPS
2008
13 years 6 months ago
2008
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
CORR
2007
Springer
13 years 5 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...
ICML
2007
IEEE
14 years 6 months ago
2007
IEEE
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the numb...