DocumentCode :
2714577
Title :
Dictionary Learning Research Based on Sparse Representation
Author :
Song, Lijuan ; Peng, Jinye
Author_Institution :
Sch. of Inf. Sci. & Technol., Northwest Univ., Xi´´an, China
fYear :
2012
fDate :
11-13 Aug. 2012
Firstpage :
14
Lastpage :
17
Abstract :
Although the number of representation subspaces is large, only few ones will contain data samples from sensor measurements. By identifying these few subspaces, we find the representation in the reduced space. We describe sparse representation and dictionary learning methods. Finally there is the experiment of the application of dictionary learning. The main focus of this thesis is the dictionary learning theory and experimental results show that the algorithm is effective.
Keywords :
dictionaries; image representation; learning (artificial intelligence); data samples; dictionary learning methods; dictionary learning research; sensor measurements; sparse signal representation; Approximation algorithms; Approximation methods; Dictionaries; Educational institutions; Learning systems; Matching pursuit algorithms; Vectors; dictionary learning; sparse representation; trained dictionary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
Type :
conf
DOI :
10.1109/CSSS.2012.12
Filename :
6394250
Link To Document :
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