A Supervised Method for Building A Regularized Map for General Multi-View Multi-Manifold 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.

AEINI FARAEIN; EFTEKHARI MOGHADAM AMIR MASOUD; MAHMOUDI FARIBORZ

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

Journal Paper

Paper Information

Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2019 | Volume:8 | Issue:4 Page(s): 1-16

Download Full-Text

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

Persian Verion

View:

304

Download:

Cites:

Information Journal Paper

Title

A Supervised Method for Building A Regularized Map for General Multi-View Multi-Manifold Learning

Author(s)

AEINI FARAEIN | EFTEKHARI MOGHADAM AMIR MASOUD | MAHMOUDI FARIBORZ | Issue Writer Certificate

Keywords

MultiQ1

view multiQ1

manifold learningQ1

Dimensionality reductionQ1

OutQ1

ofQ1

sample extensionsQ1

MultiQ1

view classificationQ1

Abstract

In this paper, we consider the issue of automatic and unsupervised class-manifold selection in a Multi-view Multi-manifold space. General Multi-manifold learning methods achieve Multiple independent manifolds, so it is challenging for them to adjust the intra-class local manifold information and global inter-class discriminative structure. In this paper, we propose a Multi-manifold embedding method, which can explicitly obtain Multi-view Multi-manifold structure while considering both intra-class compactness and inter-class separability withOut using the class label information. Furthermore, to the generalization of embedding to novel points, known as the Out-of-sample extension problem in Multi-view Multi-manifold learning, we propose a supervised method for building a regularized map that provides an Out-of-sample extension for general Multi-view Multi-manifold learning studied in the context of classification. Experimental results on face and object images demonstrate the potential of the proposed method for the classification of Multi-view Multi-manifold data sets and the proposed Out-of-sample extension algorithm for the classification of manifold-modeled data sets.

Cites

No record.

References

No record.

Cite

APA: Copy

AEINI, FARAEIN, EFTEKHARI MOGHADAM, AMIR MASOUD, & MAHMOUDI, FARIBORZ. (2019). A Supervised Method for Building A Regularized Map for General Multi-View Multi-Manifold Learning. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 8(4 ), 1-16. SID. https://sid.ir/paper/245898/en

Vancouver: Copy

AEINI FARAEIN, EFTEKHARI MOGHADAM AMIR MASOUD, MAHMOUDI FARIBORZ. A Supervised Method for Building A Regularized Map for General Multi-View Multi-Manifold Learning. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2019;8(4 ):1-16. Available from: https://sid.ir/paper/245898/en

IEEE: Copy

FARAEIN AEINI, AMIR MASOUD EFTEKHARI MOGHADAM, and FARIBORZ MAHMOUDI, “A Supervised Method for Building A Regularized Map for General Multi-View Multi-Manifold Learning,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 8, no. 4 , pp. 1–16, 2019, [Online]. Available: https://sid.ir/paper/245898/en

Related Journal Papers