Unsupervised Feature Selection Algorithm Based on Neighborhood Preserving Learning
LIU Yanfang1,2, YE Dongyi2
1.College of Mathematics and Information Engineering, Longyan University, Longyan 364012 2.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116
Abstract Since the sensitivity of neighborhood method for irrelevant features is high, an unsupervised feature selection algorithm based on neighborhood preserving learning(NPL) is proposed by utilizing the reconstruction coefficient of neighborhood to maintain the original data structure. Firstly, according to the similarity of each data and its neighborhood, the similarity matrix is constructed and a low dimensional space is built by introducing a mid-matrix. Secondly, an effective feature subset is selected by the Laplace multiplier method. Finally, the proposed algorithm is compared with six state-of-the-art feature selection methods on four publicly available datasets. Experimental results show the proposed method effectively identifies the representative features.
Fund:Supported by National Natural Science Foundation of China(No.61502104), Education Scientific Research Project of Young Teachers of Fujian Province(No.JAT170577), Climbing Project of Longyan University(No.LQ2015031,LQ2014010)
About author:: (LIU Yanfang, master, lecturer. Her research interests include machine learning, data mining and granular computing.) (YE Dongyi(Corresponding author), Ph.D., professor. His research interests include computational intelligence and data mining.)
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2018, Vol. 31 
