NDLI: Simultaneous dimensionality reduction and dictionary learning for sparse representation based classification

Content Provider	Springer Nature Link
Author	Yang, Bao Qing Gu, Chao Chen Wu, Kai Jie Zhang, Tao Guan, Xin Ping
Copyright Year	2016
Abstract	Learning dictionaries from the training data has led to promising results for pattern classification tasks. Dimensionality reduction is also an important issue for pattern classification. However, most existing methods perform dimensionality reduction (DR) and dictionary learning (DL) independently, which may result in not fully exploiting the discriminative information of the training data. In this paper, we propose a simultaneous dimensionality reduction and dictionary learning (SDRDL) model to learn a DR projection matrix and a class-specific dictionary (i.e., the dictionary atoms correspond to the class labels) simultaneously. Since simultaneously learning makes the learned projection and dictionary fit better with each other, more effective pattern classification can be achieved using the representation residual. In SDRDL model, not only the representation residual is discriminative, but the representation coefficients are also discriminative. Therefore, a classification scheme associated with SDRDL is presented by exploiting such discriminative information. Experimental results on a series of benchmark image databases show that our proposed method outperforms many state-of-the-art discriminative dictionary learning methods.
Starting Page	8969
Ending Page	8990
Page Count	22
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	76
Issue Number	6
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-04-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dictionary learning Sparse representation Dimensionality reduction Image classification Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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