10,452 research outputs found
Schatten- Quasi-Norm Regularized Matrix Optimization via Iterative Reweighted Singular Value Minimization
In this paper we study general Schatten- quasi-norm (SPQN) regularized
matrix minimization problems. In particular, we first introduce a class of
first-order stationary points for them, and show that the first-order
stationary points introduced in [11] for an SPQN regularized
minimization problem are equivalent to those of an SPQN regularized
minimization reformulation. We also show that any local minimizer of the SPQN
regularized matrix minimization problems must be a first-order stationary
point. Moreover, we derive lower bounds for nonzero singular values of the
first-order stationary points and hence also of the local minimizers of the
SPQN regularized matrix minimization problems. The iterative reweighted
singular value minimization (IRSVM) methods are then proposed to solve these
problems, whose subproblems are shown to have a closed-form solution. In
contrast to the analogous methods for the SPQN regularized
minimization problems, the convergence analysis of these methods is
significantly more challenging. We develop a novel approach to establishing the
convergence of these methods, which makes use of the expression of a specific
solution of their subproblems and avoids the intricate issue of finding the
explicit expression for the Clarke subdifferential of the objective of their
subproblems. In particular, we show that any accumulation point of the sequence
generated by the IRSVM methods is a first-order stationary point of the
problems. Our computational results demonstrate that the IRSVM methods
generally outperform some recently developed state-of-the-art methods in terms
of solution quality and/or speed.Comment: This paper has been withdrawn by the author due to major revision and
correction
An Augmented Lagrangian Approach for Sparse Principal Component Analysis
Principal component analysis (PCA) is a widely used technique for data
analysis and dimension reduction with numerous applications in science and
engineering. However, the standard PCA suffers from the fact that the principal
components (PCs) are usually linear combinations of all the original variables,
and it is thus often difficult to interpret the PCs. To alleviate this
drawback, various sparse PCA approaches were proposed in literature [15, 6, 17,
28, 8, 25, 18, 7, 16]. Despite success in achieving sparsity, some important
properties enjoyed by the standard PCA are lost in these methods such as
uncorrelation of PCs and orthogonality of loading vectors. Also, the total
explained variance that they attempt to maximize can be too optimistic. In this
paper we propose a new formulation for sparse PCA, aiming at finding sparse and
nearly uncorrelated PCs with orthogonal loading vectors while explaining as
much of the total variance as possible. We also develop a novel augmented
Lagrangian method for solving a class of nonsmooth constrained optimization
problems, which is well suited for our formulation of sparse PCA. We show that
it converges to a feasible point, and moreover under some regularity
assumptions, it converges to a stationary point. Additionally, we propose two
nonmonotone gradient methods for solving the augmented Lagrangian subproblems,
and establish their global and local convergence. Finally, we compare our
sparse PCA approach with several existing methods on synthetic, random, and
real data, respectively. The computational results demonstrate that the sparse
PCs produced by our approach substantially outperform those by other methods in
terms of total explained variance, correlation of PCs, and orthogonality of
loading vectors.Comment: 42 page
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