Please use this identifier to cite or link to this item:
Title: New semi-supervised classification using a multi-modal feature joint L-21-norm based sparse representation
Authors: Cui, Y 
Jiang, JL 
Lai, ZH 
Hu, ZJ
Jiang, YQ
Wong, WK 
Keywords: Semi-supervised classification
Label membership
Sparse representation
Issue Date: 2018
Publisher: Elsevier
Source: Signal processing. Image communication, July 2018, v. 65, p. 94-106 How to cite?
Journal: Signal processing. Image communication 
Abstract: In this paper, a novel semi-supervised classification method, namely sparse semi-supervised classification algorithm (SSSC) is proposed. To improve the reliability of SSSC, this study extends SSSC to multi-modal features joint L-21-norm based sparse representation. In the SSSC framework, the labeled patterns are sparsely represented by the abundance of unlabeled patterns, and then the scores of the unlabeled patterns are computed corresponding to the object class based on the relational degree vector. A quality measure is also presented to divide the unlabeled patterns into reliable and unreliable relabeled patterns. The reliable relabeled patterns are selected to be added into the labeled data for learning the labels of the unreliable relabeled data recurrently. Experimental results clearly demonstrate that the proposed method outperforms the state-of-the-art classification methods.
ISSN: 0923-5965
DOI: 10.1016/j.image.2018.03.005
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Citations as of Feb 18, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.