.png){kind=icon}
.png){kind=icon}
Return
A Block Minorization-Maximization Algorithm for Row-Sparse Principal Component Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2024.3431463.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a block minorization-maximization (MM) algorithm to solve the row-sparse principal component analysis (RSPCA) problem. The RSPCA problem consists of orthogonality and row-sparsity constraints. We model the decision variable as a product of a selection matrix and the matrix of principal components. This problem is solved by updating the two blocks in a cyclic manner. As the problem with respect to the selection matrix does not admit a closed-form solution, we propose to utilize the MM technique to solve this subproblem. Numerical simulations are provided to show the efficacy of the proposed algorithm.
Keywords:
Covariance matrices
Sparse matrices
Principal component analysis
Signal processing algorithms
Convergence
Linear programming
Matrix decomposition
Minorization-maximization (MM)
sparse principal component analysis
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W