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Sequential Minimax Search for Multi-Layer Gene Grouping

文献类型： 外文期刊

作者： Wang, Wenting 1 ; Zhou, Xingxing 3 ; Chen, Fuzhong 4 ; Cao, Beishao 5 ;

作者机构： 1.Shenzhen Univ, Coll Comp Sci & Software Engn, Big Data Inst, Shenzhen 518060, Peoples R China

2.Shenzhen Univ, Natl Engn Lab Big Data Syst Comp Technol, Shenzhen 518060, Peoples R China

3.Guangdong Acad Agr Sci, Guangzhou 510640, Guangdong, Peoples R China

4.Univ Int Business & Econ, Sch Int Trade & Econ, Beijing 100029, Peoples R China

5.Sun Yat Sen Univ, Dept Math, Guangzhou 510275, Guangdong, Peoples R China

关键词： Machine learning; evolutionary computing; feature grouping; high-dimensional data analysis; gene grouping; knowledge transfer

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2019 年 7 卷

页码：

收录情况： SCI

摘要： Many areas of exploratory data analysis need to deal with high-dimensional data sets. Some real life data like human gene have an inherent structure of hierarchy, which embeds multi-layer feature groups. In this paper, we propose an algorithm to search for the number of feature groups in high-dimensional data by sequential minimax method and detect the hierarchical structure of high-dimensional data. Several proper numbers of feature grouping can be discovered. The feature grouping and group weights are investigated for each group number. After the comparison of feature groupings, the multi-layer structure of feature groups is detected. The latent feature group learning (LFGL) algorithm is proposed to evaluate the effectiveness of the number of feature groups and provide a method of subspace clustering. In the experiments on several gene data sets, the proposed algorithm outstands several representative algorithms.

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