Label Distribution Learning by Regularized Sample Self-Representation
Joint Authors
Yang, Wenyuan
Li, Chan
Zhao, Hong
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Multilabel learning that focuses on an instance of the corresponding related or unrelated label can solve many ambiguity problems.
Label distribution learning (LDL) reflects the importance of the related label to an instance and offers a more general learning framework than multilabel learning.
However, the current LDL algorithms ignore the linear relationship between the distribution of labels and the feature.
In this paper, we propose a regularized sample self-representation (RSSR) approach for LDL.
First, the label distribution problem is formalized by sample self-representation, whereby each label distribution can be represented as a linear combination of its relevant features.
Second, the LDL problem is solved by L2-norm least-squares and L2,1-norm least-squares methods to reduce the effects of outliers and overfitting.
The corresponding algorithms are named RSSR-LDL2 and RSSR-LDL21.
Third, the proposed algorithms are compared with four state-of-the-art LDL algorithms using 12 public datasets and five evaluation metrics.
The results demonstrate that the proposed algorithms can effectively identify the predictive label distribution and exhibit good performance in terms of distance and similarity evaluations.
American Psychological Association (APA)
Yang, Wenyuan& Li, Chan& Zhao, Hong. 2018. Label Distribution Learning by Regularized Sample Self-Representation. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1205494
Modern Language Association (MLA)
Yang, Wenyuan…[et al.]. Label Distribution Learning by Regularized Sample Self-Representation. Mathematical Problems in Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1205494
American Medical Association (AMA)
Yang, Wenyuan& Li, Chan& Zhao, Hong. Label Distribution Learning by Regularized Sample Self-Representation. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1205494
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205494