| L2HalfThr {JSparO} | R Documentation |
L2HalfThr - Iterative Half Thresholding Algorithm based on l_{2,1/2} norm
Description
The function aims to solve l_{2,1/2} regularized least squares.
Usage
L2HalfThr(A, B, X, s, maxIter = 200)
Arguments
A |
Gene expression data of transcriptome factors (i.e. feature matrix in machine learning). The dimension of A is m * n. |
B |
Gene expression data of target genes (i.e. observation matrix in machine learning). The dimension of B is m * t. |
X |
Gene expression data of Chromatin immunoprecipitation or other matrix (i.e. initial iterative point in machine learning). The dimension of X is n * t. |
s |
joint sparsity level |
maxIter |
maximum iteration |
Details
The L2HalfThr function aims to solve the problem:
\min \|AX-B\|_F^2 + \lambda \|X\|_{2,1/2}
to obtain s-joint sparse solution.
Value
The solution of proximal gradient method with l_{2,1/2} regularizer.
Author(s)
Xinlin Hu thompson-xinlin.hu@connect.polyu.hk
Yaohua Hu mayhhu@szu.edu.cn
Examples
m <- 256; n <- 1024; t <- 5; maxIter0 <- 50
A0 <- matrix(rnorm(m * n), nrow = m, ncol = n)
B0 <- matrix(rnorm(m * t), nrow = m, ncol = t)
X0 <- matrix(0, nrow = n, ncol = t)
NoA <- norm(A0, '2'); A0 <- A0/NoA; B0 <- B0/NoA
res_L2half <- L2HalfThr(A0, B0, X0, s = 10, maxIter = maxIter0)