Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67347
Title: A locality-constrained and label embedding dictionary learning algorithm for image classification
Authors: Li, ZM
Lai, ZH
Xu, Y
Yang, J
Zhang, D 
Keywords: Dictionary learning
Label embedding
Locality constrained
Profile
Sparse coding
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2017, v. 28, no. 2, p. 278-293 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.
URI: http://hdl.handle.net/10397/67347
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2015.2508025
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