Application of Sparse Representation in Bioinformatics

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

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Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

Remote Sensing ◽

10.3390/rs12233991 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3991

Author(s):

Xiaobin Zhao ◽

Wei Li ◽

Mengmeng Zhang ◽

Ran Tao ◽

Pengge Ma

Keyword(s):

Sparse Representation ◽

Target Detection ◽

Matched Filter ◽

Hyperspectral Imagery ◽

Low Rank ◽

Collaborative Representation ◽

Support Vector ◽

L1 Norm ◽

Lp Norm ◽

Norm Minimization

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving l1-norm minimization. However, l1-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the lp penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with lp-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for lp-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about 10% to 30% than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with l1-norm, and combined sparse and collaborative representation (CSCR).

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Sparse and Low-Rank Coupling Image Segmentation Model Via Nonconvex Regularization

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415550046 ◽

2015 ◽

Vol 29 (02) ◽

pp. 1555004 ◽

Cited By ~ 18

Author(s):

Xiujun Zhang ◽

Chen Xu ◽

Min Li ◽

Xiaoli Sun

Keyword(s):

Image Segmentation ◽

Sparse Representation ◽

Optimal Solution ◽

Low Rank ◽

Public Image ◽

Representation Problem ◽

Norm Minimization ◽

Augmented Lagrange Multiplier ◽

Nonconvex Regularization ◽

Low Rank Representation

This paper investigates how to boost region-based image segmentation by inheriting the advantages of sparse representation and low-rank representation. A novel image segmentation model, called nonconvex regularization based sparse and low-rank coupling model, is presented for such a purpose. We aim at finding the optimal solution which is provided with sparse and low-rank simultaneously. This is achieved by relaxing sparse representation problem as L1/2 norm minimization other than the L1 norm minimization, while relaxing low-rank representation problem as the S1/2 norm minimization other than the nuclear norm minimization. This coupled model can be solved efficiently through the Augmented Lagrange Multiplier (ALM) method and half-threshold operator. Compared to the other state-of-the-art methods, the new method is better at capturing the global structure of the whole data, the robustness is better and the segmentation accuracy is also competitive. Experiments on two public image segmentation databases well validate the superiority of our method.

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