Bio-Inspired Structure Representation Based Cross-View Discriminative Subspace Learning via Simultaneous Local and Global Alignment

Joint Authors

Li, Ao
Ding, Yu
Zheng, Xunjiang
Chen, Deyun
Sun, Guanglu
Lin, Kezheng

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-04

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Philosophy

Abstract EN

Recently, cross-view feature learning has been a hot topic in machine learning due to the wide applications of multiview data.

Nevertheless, the distribution discrepancy between cross-views leads to the fact that instances of the different views from same class are farther than those within the same view but from different classes.

To address this problem, in this paper, we develop a novel cross-view discriminative feature subspace learning method inspired by layered visual perception from human.

Firstly, the proposed method utilizes a separable low-rank self-representation model to disentangle the class and view structure layers, respectively.

Secondly, a local alignment is constructed with two designed graphs to guide the subspace decomposition in a pairwise way.

Finally, the global discriminative constraint on distribution center in each view is designed for further alignment improvement.

Extensive cross-view classification experiments on several public datasets prove that our proposed method is more effective than other existing feature learning methods.

American Psychological Association (APA)

Li, Ao& Ding, Yu& Zheng, Xunjiang& Chen, Deyun& Sun, Guanglu& Lin, Kezheng. 2020. Bio-Inspired Structure Representation Based Cross-View Discriminative Subspace Learning via Simultaneous Local and Global Alignment. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1145063

Modern Language Association (MLA)

Li, Ao…[et al.]. Bio-Inspired Structure Representation Based Cross-View Discriminative Subspace Learning via Simultaneous Local and Global Alignment. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1145063

American Medical Association (AMA)

Li, Ao& Ding, Yu& Zheng, Xunjiang& Chen, Deyun& Sun, Guanglu& Lin, Kezheng. Bio-Inspired Structure Representation Based Cross-View Discriminative Subspace Learning via Simultaneous Local and Global Alignment. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1145063

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1145063