Published on Tue Aug 20 2019

Non-negative Sparse and Collaborative Representation for Pattern Classification

Jun Xu, Zhou Xu, Wangpeng An, Haoqian Wang, David Zhang

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. We propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification.

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Abstract

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification. The NSCR representation of each test sample is obtained by seeking a non-negative sparse and collaborative representation vector that represents the test sample as a linear combination of training samples. We observe that the non-negativity can make the SR and CR more discriminative and effective for pattern classification. Based on the proposed NSCR, we propose a NSCR based classifier for pattern classification. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR based classifier outperforms the previous SR or CR based approach, as well as state-of-the-art deep approaches, on diverse challenging pattern classification tasks.

Tue Jun 12 2018
Computer Vision
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
The use of sparse representation (SR) and collaborative representation (CR) has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the sparsity or collaborative property that brings the
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Thu Mar 06 2014
Artificial Intelligence
Collaborative Representation for Classification, Sparse or Non-sparse?
Sparse representation based classification (SRC) has been proved to be asimple, effective and robust solution to face recognition. Given a new classification task, it's still unclear which regularization strategy (i.e., making the coefficients sparse or non-sparse) is a better choice.
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Fri Apr 18 2014
Computer Vision
Robust Face Recognition via Adaptive Sparse Representation
Adaptive Sparse Representation based Classification (ASRC) combines sparsity and correlation. When the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples. Extensive experiments conducted on publicly available data sets verify the effectiveness of the proposed algorithm.
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Sun Nov 29 2015
Computer Vision
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method.
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Wed Apr 11 2012
Computer Vision
Collaborative Representation based Classification for Face Recognition
S sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC.
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Tue Mar 19 2019
Computer Vision
Non-negative representation based discriminative dictionary learning for face recognition
In this paper, we propose a non-negative representation based discriminativeisivelydictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non- negative representation (NR)
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