Volume 12, Issue 3 (9-2020)                   itrc 2020, 12(3): 38-46 | Back to browse issues page

XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zare H, Ghasemi Parsa G P, Ghatee M, H. Alizadeh S. Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning. itrc 2020; 12 (3) :38-46
URL: http://journal.itrc.ac.ir/article-1-465-en.html
1- Faculty of New Sciences and Technologies University of Tehran Tehran, Iran , h.zare@ut.ac.ir
2- Faculty of New Sciences and Technologies University of Tehran Tehran, Iran
3- Department of Computer Science Amirkabir University of Technology Tehran, Iran
4- Department of Information Technology IRAN Telecommunication Research Center Tehran, Iran
Abstract:   (1918 Views)
 
Abstract— Nowadays, we face a huge number of high-dimensional data in different applications and technologies. To tackle the challenge, various feature selection methods have been recently proposed for reducing the computational complexity of the learning algorithms as well as simplifying the learning models. Maintaining the geometric structures and considering the discriminative information in data are two important factors that should be borne in mind particularly for unsupervised feature selection methods. In this paper, our aim is to propose a new unsupervised feature selection approach by considering global and local similarities and discriminative information. Furthermore, this unsupervised framework incorporates cluster analysis to consider the underlying structure of the samples. Moreover, the correlation of features and clusters is computed by an -norm regularized regression to eliminate the redundant and irrelevant features. Finally, a unified objective function is presented as well as an efficient iterative optimization algorithm to solve the corresponding problem with some theoretical analysis of the convergence and the complexity of the algorithm. We compare the proposed approach with the state-of-the-art method based on clustering results on the various standard datasets including biology, image, voice, and artificial data. The experimental results have presented the strength and performance improvement of the proposed method by outperforming the well-known methods
Full-Text [PDF 1217 kb]   (861 Downloads)    
Type of Study: Research | Subject: Information Technology

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.