L2SoftThr function

L2SoftThr - Iterative Soft Thresholding Algorithm based on $l_{2,1}$ norm

The function aims to solve $l_{2,1}$ regularized least squares.

L2SoftThr(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

Returns

The solution of proximal gradient method with $l_{2,1}$ regularizer.

Details

The L2SoftThr function aims to solve the problem:

to obtain s-joint sparse solution.

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_L21 <- L2SoftThr(A0, B0, X0, s = 10, maxIter = maxIter0)

Author(s)

Xinlin Hu thompson-xinlin.hu@connect.polyu.hk

Yaohua Hu mayhhu@szu.edu.cn