Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a L...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 1; s. 92 - 104
Hlavní autori:	Shenghua Gao, Tsang, Ivor Wai-Hung, Liang-Tien Chia
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.01.2013 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Artificial Intelligence Coding Coding, codes Computer science; control theory; systems Data Compression - methods Data processing. List processing. Character string processing Decision Support Techniques Encoding Exact sciences and technology Graphs hypergraph Laplacian sparse coding Image classification Image coding Image Interpretation, Computer-Assisted - methods Image reconstruction Information retrieval. Graph Information, signal and communications theory Laplace equations Laplacian sparse coding locality preserving Memory organisation. Data processing Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Preserves Quantization Representations Robustness semi-auto image tagging Signal and communications theory Similarity Software Sparse matrices Studies Tagging Telecommunications and information theory Theoretical computing Hypergraph Computer vision Image processing Laplacian sparse coding locality preserving Locality Information retrieval Laplacian Bag of words Codebook semi-auto image tagging Signal quantization Tagging hypergraph Laplacian sparse coding Sparse representation Robustness Image classification
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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