Unsupervised feature selection approach based on spectral analysis

doi:10.3724/SP.J.1087.2011.02108

Journal of Computer Applications ›› 2011, Vol. 31 ›› Issue (08): 2108-2110.DOI: 10.3724/SP.J.1087.2011.02108

• Artificial intelligence • Previous Articles Next Articles

Unsupervised feature selection approach based on spectral analysis

Feng PAN¹,²,Jiang-dong WANG¹,Ben NIU²

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China
2. College of Management, Shenzhen University, Shenzhen Guangdong 518060, China

Received:2011-02-15 Revised:2011-04-13 Online:2011-08-01 Published:2011-08-01
Contact: Feng PAN

基于谱分析的无监督特征选择算法

潘锋¹,²,王建东¹,牛奔²

1. 南京航空航天大学计算机科学与技术学院，南京210016
2. 深圳大学管理学院，广东深圳518060

通讯作者: 潘锋
作者简介:潘锋(1977-)，男，河南正阳人，讲师，博士研究生，主要研究方向：数据挖掘、机器学习；王建东(1945-)，男，江苏南京人，教授，博士生导师，主要研究方向：数据挖掘、机器学习、知识工程；牛奔(1980-)，男，安徽全椒人，副教授，博士，主要研究方向：智能系统、人工生命。
基金资助:
国家自然科学基金资助项目(60803092);广东省自然科学基金资助项目(9151170003000017)

Abstract

Abstract: To improve the performance of feature selection under the unsupervised scenario, the relationship between the distribution of the first K minimal eigenvalues for a normalized graph Laplacian matrix and the structure of the clusters was identified, and a new feature selection algorithm based on the spectral analysis was proposed. The feature selection algorithm might be time-consuming; hence the Nystrm method was applied to reduce the computational cost of the eigen-decomposition. The experiments on synthetic and real-world data sets show the efficiency of the proposed approach.

Key words: data mining, cluster algorithm, unsupervised learning, spectral analysis, feature selection

摘要： 为提高无监督状态下特征选择的准确度，基于图谱理论证明了标准化Laplacian矩阵前K个最小特征值分布表现了簇结构的可识别性，使用谱分析方法指导无监督特征选择，提出了特征重要度排序算法。对大容量数据集特征选择，应用Nystrm方法降低时间复杂度。实验结果表明，使用该算法与使用主流无监督特征选择方法及全部特征相比能得到更高的聚类性能评分。

关键词: 数据挖掘, 聚类算法, 无监督学习, 谱分析, 特征选择

CLC Number:

TP18
TP301.6

Feng PAN Jiang-dong WANG Ben NIU. Unsupervised feature selection approach based on spectral analysis[J]. Journal of Computer Applications, 2011, 31(08): 2108-2110.

潘锋王建东牛奔. 基于谱分析的无监督特征选择算法[J]. 计算机应用, 2011, 31(08): 2108-2110.

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Unsupervised feature selection approach based on spectral analysis

基于谱分析的无监督特征选择算法

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