联合依赖最大化与稀疏表示的无监督特征选择方法

南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (4): 775–.

联合依赖最大化与稀疏表示的无监督特征选择方法

李　婵，杨文元*，赵　红

出版日期:2017-08-03 发布日期:2017-08-03
作者简介: 闽南师范大学粒计算重点实验室，漳州，363000
基金资助:
基金项目：国家自然科学基金(61379049，61379089)
收稿日期：2017－06－09
*通讯联系人，E－mail：yangwy@xmu.edu.cn

Unsupervised feature selection method via dependence maximization and sparse representation

Li Chan，Yang Wenyuan*，Zhao Hong

Online:2017-08-03 Published:2017-08-03
About author: Laboratory of Granular Computing，Minnan Normal University，Zhangzhou，363000，China

摘要/Abstract

摘要： 高维数据分析任务中，无监督特征选择是一项重要并具有挑战性的任务．传统的无监督特征选择算法通过保持流形结构或者特征之间相关性进行特征选择，而没有直接考虑选择特征与原始数据的依赖程度．通过考虑投影后的低维空间数据与原始数据信息之间的依赖性，提出有良好性能的特征依赖于原始数据的度量原则．首先利用最大化依赖使投影后数据尽可能保持原始数据的特征信息，据此获得投影矩阵，从而对原始数据达到降维效果．然后联合稀疏表示进行特征选择．提出一种新的无监督特征选择算法，称之为联合依赖最大化与稀疏表示的无监督特征选择方法(DMSR)．在4个实际的数据集上进行实验，并与3种已有的无监督特征选择算法进行比较，在两种评价指标聚类精度和互信息上的实验结果表明，提出的DMSR算法是有效的．

Abstract: Unsupervised feature selection is an important and challenging task in high dimensional data analysis tasks.The traditional unsupervised feature selection algorithms make a feature selection by keeping manifold structure or the correlation in features，however，it fails to directly think about the dependence between the selected features and the original data.In contrast，we consider the dependence between original data and low dimensional spatial data after projection，and propose a measurement principle that the feature with good performance based on the original data in this paper.First，we make the projected data retain the characteristic information of the original data as much as possible by maximizing the dependence to count projection matrix.Thus the dimensionality reduction effect is achieved for the original data.Then，we combine sparse representation to make feature selection and put forward an unsupervised feature selection algorithm.The proposed algorithm is termed as unsupervised feature selection method via dependence maximization and sparse representation(DMSR)．Finally，experiments are carried out on four public data sets to compare with three existing unsupervised feature selection algorithms.The experimental results on two evaluation indexes，which are clustering accuracy and mutual information show that the proposed DMSR algorithm is effective.

李　婵，杨文元*，赵　红. 联合依赖最大化与稀疏表示的无监督特征选择方法[J]. 南京大学学报(自然科学版), 2017, 53(4): 775–.

Li Chan，Yang Wenyuan*，Zhao Hong. Unsupervised feature selection method via dependence maximization and sparse representation[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(4): 775–.

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