Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder

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.

Farajian Nima; ADIBI PEYMAN

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:2019 | Volume:11 | Issue:2 Page(s): 29-37

Download Full-Text

View:

183

Download:

Cites:

Information Journal Paper

Title

Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder

Author(s)

Farajian Nima | ADIBI PEYMAN | Issue Writer Certificate

Keywords

component

Supervised Auto-encoder

Feature Learning

Discriminative Representation

Manifold

Abstract

Recent researches have determined that regularized auto-encoders can provide a good representation of data which improves the performance of data classification. These type of auto-encoders provides a representation of data that has some degree of sparsity and is robust against variation of data to extract useful information and reveal the underlying structure of data. The present study aimed to propose a novel approach to generate sparse, robust, and discriminative features through supervised regularized auto-encoders, in which unlike most existing auto-encoders, the data labels are used during feature extraction to improve discrimination of the representation and also, the sparsity ratio of the representation is completely adaptive with data distribution. Results reveal that this method has better performance in comparison to other regularized auto-encoders regarding data classification.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Farajian, Nima, & ADIBI, PEYMAN. (2019). Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 11(2), 29-37. SID. https://sid.ir/paper/770994/en

Vancouver: Copy

Farajian Nima, ADIBI PEYMAN. Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2019;11(2):29-37. Available from: https://sid.ir/paper/770994/en

IEEE: Copy

Nima Farajian, and PEYMAN ADIBI, “Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 11, no. 2, pp. 29–37, 2019, [Online]. Available: https://sid.ir/paper/770994/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops