demo_JSparO function

demo_JSparO - The demo of JSparO package

This is the main function of JSparO aimed to solve the low-order regularization models with $l_{p,q}$ norm.

demo_JSparO(A, B, X, s, p, q, 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
• p: value for $l_{p,q}$ norm (i.e., p = 1 or 2)
• q: value for $l_{p,q}$ norm (i.e., 0 <= q <= 1)
• maxIter: maximum iteration

Returns

The solution of proximal gradient method with $l_{p,q}$ regularizer.

Details

The demo_JSparO function is used to solve joint sparse optimization problem via different algorithms. Based on $l_{p,q}$ norm, functions with different p and q are implemented 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)
res_JSparO <- demo_JSparO(A0, B0, X0, s = 10, p = 2, q = 'half', maxIter = maxIter0)

Author(s)

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

Yaohua Hu mayhhu@szu.edu.cn