Robust Exemplar Extraction Using Structured Sparse Coding

Robust exemplar extraction from the noisy sample set is one of the most important problems in pattern recognition. In this brief, we propose a novel approach for exemplar extraction through structured sparse learning. The new model accounts for not only the reconstruction capability and the sparsity...

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Bibliographic Details
Published inIEEE transaction on neural networks and learning systems Vol. 26; no. 8; pp. 1816 - 1821
Main Authors Liu, Huaping, Liu, Yunhui, Sun, Fuchun
Format Journal Article
LanguageEnglish
Published United States IEEE 01.08.2015
Subjects
Alternating directional method of multiplier (ADMM)
Data mining
Encoding
Noise measurement
Optimization
robust exemplar extraction
Robustness
structured sparse coding
traffic sign recognition
Training
Vectors
traffic sign recognition
structured sparse coding
Alternating directional method of multiplier (ADMM)
robust exemplar extraction
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Summary:Robust exemplar extraction from the noisy sample set is one of the most important problems in pattern recognition. In this brief, we propose a novel approach for exemplar extraction through structured sparse learning. The new model accounts for not only the reconstruction capability and the sparsity, but also the diversity and robustness. To solve the optimization problem, we adopt the alternating directional method of multiplier technology to design an iterative algorithm. Finally, the effectiveness of the approach is demonstrated by experiments of various examples including traffic sign sequences.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2014.2357036