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Multilabel Classification Using Low-Rank Decomposition
Joint Authors
Yang, Bo
Tong, Kunkun
Zhao, Xueqing
Pang, Shanmin
Chen, Jinguang
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-07
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In the multilabel learning framework, each instance is no longer associated with a single semantic, but rather with concept ambiguity.
Specifically, the ambiguity of an instance in the input space means that there are multiple corresponding labels in the output space.
In most of the existing multilabel classification methods, a binary annotation vector is used to denote the multiple semantic concepts.
That is, +1 denotes that the instance has a relevant label, while −1 means the opposite.
However, the label representation contains too little semantic information to truly express the differences among multiple different labels.
Therefore, we propose a new approach to transform binary label into a real-valued label.
We adopt the low-rank decomposition to get latent label information and then incorporate the information and original features to generate new features.
Then, using the sparse representation to reconstruct the new instance, the reconstruction error can also be applied in the label space.
In this way, we finally achieve the purpose of label conversion.
Extensive experiments validate that the proposed method can achieve comparable to or even better results than other state-of-the-art algorithms.
American Psychological Association (APA)
Yang, Bo& Tong, Kunkun& Zhao, Xueqing& Pang, Shanmin& Chen, Jinguang. 2020. Multilabel Classification Using Low-Rank Decomposition. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1152797
Modern Language Association (MLA)
Yang, Bo…[et al.]. Multilabel Classification Using Low-Rank Decomposition. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1152797
American Medical Association (AMA)
Yang, Bo& Tong, Kunkun& Zhao, Xueqing& Pang, Shanmin& Chen, Jinguang. Multilabel Classification Using Low-Rank Decomposition. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1152797
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152797