基因表达数据的低秩投影最小二乘回归子空间分割<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201702002

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Abstract The traditional clustering methods are inefficient due to high dimension and redundancy, small sample size and noise of the gene expression data. Subspace segmentation is an effective method for high dimensional data clustering. However, the performance of clustering is reduced by using subspace segmentation on the gene expression data directly. To cluster the gene expression data more effectively, low rank projection least square regression subspace segmentation method(LPLSR) is proposed. The improved low rank method is utilized to project gene expression data into the latent subspace to remove the possible corruptions in data and get a relatively clean data dictionary. Then, least square regression method is employed to obtain the low-dimension representation for data vectors and the affinity matrix is constructed to cluster the gene data. The experimental results on six public gene expression datasets show the validity of the proposed method.

Key words： Gene Expression Data Clustering Least Square Regression Subspace Segmentation Low Rank Projection

Received: 19 July 2016

ZTFLH:	TP 311
	TP 371

Fund:Supported by National Natural Science Foundation of China(No.11571074,71273053), Natural Science Foundation of Fujian Province(No.2014J01009)

About author:: (CHEN Xiaoyun(Corresponding author), born in 1970, Ph.D., professor. Her research interests include data mining, pattern recognition and machine learning.)(XIAO Bingsen, born in 1992, master student. His research interests include data mining and pattern recognition.)(LIN Liyuan, born in 1991, master student. Her research interests include data mining and pattern recognition.)

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	CHEN Xiaoyun
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	LIN Liyuan

Cite this article:

CHEN Xiaoyun,XIAO Bingsen,LIN Liyuan. Low Rank Projection Least Square Regression Subspace Segmentation
for Gene Expression Data[J]. , 2017, 30(2): 106-116.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201702002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I2/106

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