10 search results - page 1 / 2 » Full regularization path for sparse principal component anal... |
ICML
2007
IEEE
14 years 5 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...
JMLR
2010
12 years 11 months ago
2010
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
CORR
2007
Springer
13 years 4 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...
JMLR
2010
12 years 11 months ago
2010
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
ECML
2007
Springer
13 years 10 months ago
2007
Springer
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...