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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۲، شماره ۳، صفحات ۳۸-۴۶
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning |
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| چکیده انگلیسی مقاله |
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 |
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| کلیدواژه‌های انگلیسی مقاله |
Unsupervised feature selection, Similarity preserving, Low dimensional embedding, Cluster analysis, Sparse learningPPDM. |
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| نویسندگان مقاله |
| Hadi Zare
Faculty of New Sciences and Technologies University of Tehran Tehran, Iran
| Ghasemi Parsa Ghasemi Parsa
Faculty of New Sciences and Technologies University of Tehran Tehran, Iran
| Mehdi Ghatee
Department of Computer Science Amirkabir University of Technology Tehran, Iran
| Sasan H. Alizadeh
Department of Information Technology IRAN Telecommunication Research Center Tehran, Iran
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| نشانی اینترنتی |
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4256-1&slc_lang=other&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
other |
| موضوعات مقاله منتشر شده |
فناوری اطلاعات |
| نوع مقاله منتشر شده |
پژوهشی |
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