287 search results - page 5 / 58 » Optimal Solutions for Sparse Principal Component Analysis |
JMLR
2012
13 years 1 days ago
2012
We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove o...
117
click to vote
CORR
2012
Springer
13 years 5 months ago
2012
Springer
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
ICML
2007
IEEE
15 years 10 months 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...
ICASSP
2009
IEEE
15 years 1 months ago
2009
IEEE
We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. ...
114
click to vote
IJCNN
2006
IEEE
15 years 3 months ago
2006
IEEE
— We present a new optimization procedure which is particularly suited for the solution of second-order kernel methods like e.g. Kernel-PCA. Common to these methods is that there...