L1HardThr function

L1HardThr - Iterative Hard Thresholding Algorithm based on l1,0l_{1,0} norm

L1HardThr - Iterative Hard Thresholding Algorithm based on l1,0l_{1,0} norm

The function aims to solve l1,0l_{1,0} regularized least squares.

L1HardThr(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 l1,0l_{1,0} regularizer.

Details

The L1HardThr function aims to solve the problem:

minAXBF2+λX1,0 \min \|AX-B\|_F^2 + \lambda \|X\|_{1,0}

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_L10 <- L1HardThr(A0, B0, X0, s = 10, maxIter = maxIter0)

Author(s)

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

Yaohua Hu mayhhu@szu.edu.cn

  • Maintainer: Xinlin Hu
  • License: GPL (>= 3)
  • Last published: 2022-08-18

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