Group K-SVD for the classification of gene expression data

被引:6
作者
He, Ping [1 ]
Fan, Baichuan [1 ]
Xu, Xiaohua [1 ]
Ding, Jie [2 ]
Liang, Yali [1 ]
Lou, Yuan [1 ]
Zhang, Zhijun [1 ]
Chang, Xincheng [1 ]
机构
[1] Yangzhou Univ, Dept Comp Sci, Yangzhou, Jiangsu, Peoples R China
[2] Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai, Peoples R China
关键词
Classification; K-SVD; Gene expression data; Dictionary learning; Sparse representation; SPARSE; ALGORITHM;
D O I
10.1016/j.compeleceng.2019.03.009
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a new sparse learning model for the classification of gene expression data, named Group K-Singular Value Decomposition (Group K-SVD). It learns the optimal dictionary and sparse representation from the training data, and then assigns the out-of-sample data to the class with the nearest centroid. Group K-SVD reduces the redundancy of over-complete dictionary by using a group update strategy during the dictionary update stage. To solve the optimization problem, we also develop a Multivariate Orthogonal Matching Pursuit (MOMP) algorithm for the sparse coefficient update stage. In the evaluation of the performance of Group K-SVD model, we implement three Group K-SVD algorithms by incorporating MOMP as well as another two popular optimization algorithms into the sparse coefficient update stage. Sufficient experimental results demonstrate that our Group K-SVD algorithms are both effective and efficient in comparison with the state-of-the-methods on real-world gene expression datasets. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:143 / 153
页数:11
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