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» Deflation Methods for Sparse PCA
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NIPS
2008
11 years 2 months ago
Deflation Methods for Sparse PCA
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...
Lester Mackey
CORR
2010
Springer
143views Education» more  CORR 2010»
10 years 10 months ago
CUR from a Sparse Optimization Viewpoint
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...
Jacob Bien, Ya Xu, Michael W. Mahoney
CORR
2010
Springer
136views Education» more  CORR 2010»
10 years 10 months ago
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors a...
Matthias Hein, Thomas Bühler
PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
10 years 12 months ago
Sparse Unsupervised Dimensionality Reduction Algorithms
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...
Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang
SIAMMAX
2010
146views more  SIAMMAX 2010»
10 years 8 months ago
A Comparison of Two-Level Preconditioners Based on Multigrid and Deflation
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...
J. M. Tang, S. P. MacLachlan, Reinhard Nabben, C. ...
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