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