502 search results - page 4 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
100
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JMLR
2010
14 years 4 months 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...
112
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JMLR
2012
13 years 1 days 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...
101
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NIPS
2008
14 years 11 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
14 years 9 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
15 years 10 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...