Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ZARE HADI; Ghasemi Parsa Mohsen; GHATEE MEHDI; Alizadeh Sasan H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Information and Communication Technology Research Year:2020 | Volume:12 | Issue:3 Page(s): 38-46

Download Full-Text

View:

109

Download:

Cites:

Information Journal Paper

Title

Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning

Author(s)

ZARE HADI | Ghasemi Parsa Mohsen | GHATEE MEHDI | Alizadeh Sasan H. | Issue Writer Certificate

Keywords

Unsupervised feature selection

Similarity preserving

Low dimensional embedding

Cluster analysis

Sparse learning

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.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

ZARE, HADI, Ghasemi Parsa, Mohsen, GHATEE, MEHDI, & Alizadeh, Sasan H.. (2020). Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 12(3), 38-46. SID. https://sid.ir/paper/969984/en

Vancouver: Copy

ZARE HADI, Ghasemi Parsa Mohsen, GHATEE MEHDI, Alizadeh Sasan H.. Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2020;12(3):38-46. Available from: https://sid.ir/paper/969984/en

IEEE: Copy

HADI ZARE, Mohsen Ghasemi Parsa, MEHDI GHATEE, and Sasan H. Alizadeh, “Unsupervised Feature Selection Based on Low Dimensional Embedding and Subspace Learning,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 12, no. 3, pp. 38–46, 2020, [Online]. Available: https://sid.ir/paper/969984/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops