40 search results - page 1 / 8 » Deflation Methods for Sparse PCA |
NIPS
2008
13 years 5 months ago
2008
In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization and m...
CORR
2010
Springer
13 years 1 months ago
2010
Springer
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span o...
CORR
2010
Springer
13 years 1 months ago
2010
Springer
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors a...
PKDD
2010
Springer
13 years 2 months ago
2010
Springer
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...
SIAMMAX
2010
12 years 11 months ago
2010
It is well-known that two-level and multi-level preconditioned conjugate gradient (PCG) methods provide efficient techniques for solving large and sparse linear systems whose coeff...