模式识别与人工智能
Friday, May. 2, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2018, Vol. 31 Issue (2): 114-122    DOI: 10.16451/j.cnki.issn1003-6059.201802002
Papers and Reports Current Issue| Next Issue| Archive| Adv Search |
Overlapping Subspace Clustering Based on Local Weighted Least Squares Regression
QIU Yunfei1,2, FEI Bowen2, LIU Daqian3
1.School of Software, Liaoning Technical University, Huludao 125105
2.School of Business Administration, Liaoning Technical University, Huludao 125105
3.School of Electronics and Information Engineering, Liaoning Technical University, Huludao 125105

Download: PDF (716 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  

Most subspace clustering methods can not deal with nonlinear data satisfactorily, and the data in different subspaces possess higher similarity and clustering error can not be verified in time. Aiming at these problems, an overlapping subspace clustering algorithm based on local weighted least squares regression(LWLSR) is proposed. The k-nearest neighbor(KNN) is introduced to highlight the local information of data and replace the nonlinear data structure. The nearest neighbor data points are selected by the Gaussian weighting method to obtain the optimal representation coefficients. Then, an overlapping probability model is employed to determine the overlap of the data in the subspace, and the clustering results are rechecked to improve the clustering accuracy. The experimental results on both artificial datasets and real-world datasets show that the proposed algorithm achieves better clustering results.

Key wordsOverlapping Subspace Clustering      K-Nearest Neighbor      Gaussian Weighting      Overlapping Probability Model     
Received: 11 September 2017     
ZTFLH: TP 181  
Fund:

Supported by Young Scientists Fund of National Natural Science Foundation of China(No.61401185)

About author:: QIU Yunfei(Corresponding author), Ph.D., professor. His research interests include data mining and intelligent data processing.FEI Bowen, Ph.D. candidate. Her research interests include data mining and intelligent data processing.LIU Daqian, Ph.D. candidate. His research interests include image and vision computing, target detection and tracking.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
QIU Yunfei
FEI Bowen
LIU Daqian
Cite this article:   
QIU Yunfei,FEI Bowen,LIU Daqian. Overlapping Subspace Clustering Based on Local Weighted Least Squares Regression[J]. , 2018, 31(2): 114-122.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802002      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/114
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn