title: Similarity-balanced discriminant neighbor embedding and its application to cancer classification based on gene expression data.
abstract: The family of discriminant neighborhood embedding (DNE) methods is typical graph-based methods for dimension reduction, and has been successfully applied to face recognition. This paper proposes a new variant of DNE, called similarity-balanced discriminant neighborhood embedding (SBDNE) and applies it to cancer classification using gene expression data. By introducing a novel similarity function, SBDNE deals with two data points in the same class and the different classes with different ways. The homogeneous and heterogeneous neighbors are selected according to the new similarity function instead of the Euclidean distance. SBDNE constructs two adjacent graphs, or between-class adjacent graph and within-class adjacent graph, using the new similarity function. According to these two adjacent graphs, we can generate the local between-class scatter and the local within-class scatter, respectively. Thus, SBDNE can maximize the between-class scatter and simultaneously minimize the within-class scatter to find the optimal projection matrix. Experimental results on six microarray datasets show that SBDNE is a promising method for cancer classification.
authors.affiliation: School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China; Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, Jiangsu, China. Electronic address: zhangliml@suda.edu.cn.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China.
authors.forename: Liqiang
authors.initials: L
authors.lastname: Qian
authors.affiliation: School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China.
authors.forename: Chuntao
authors.initials: C
authors.lastname: Ding
authors.affiliation: AI Speech Ltd., Suzhou 215123, Jiangsu, China.
authors.forename: Weida
authors.initials: W
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China; Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, Jiangsu, China.
authors.forename: Fanzhang
authors.initials: F
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis
keywords: Adjacent graph; Cancer classification; Discriminant neighborhood embedding; Gene expression data; Microarray data