Block recursive least squares dictionary learning algorithm

The block recursive least square (BRLS) dictionary learning algorithm that dealing with training data arranged in block is proposed in this paper. BRLS can be used to update overcomplete dictionary for sparse signal representation. Different from traditional recursive least square algorithms, BRLS i...

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Veröffentlicht in:Chinese Control and Decision Conference S. 1961 - 1964
Hauptverfasser: Jiang, Qianru, Li, Sheng, Lu, Zeru, Sun, Binbin
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2016
Schlagworte:
Algorithm design and analysis
Algorithms
block
Conferences
Dictionaries
Electronics
Learning
Least squares method
Matching pursuit algorithms
Overcomplete dictionary
Recursion
recursive least square
Representations
Signal to noise ratio
Sparse matrices
sparse representation
Training
Training data
ISSN:1948-9447
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Zusammenfassung:The block recursive least square (BRLS) dictionary learning algorithm that dealing with training data arranged in block is proposed in this paper. BRLS can be used to update overcomplete dictionary for sparse signal representation. Different from traditional recursive least square algorithms, BRLS is designed for data in a block form and the recursion is developed without using the matrix inversion lemma. The proposed algorithm is applied in synthetic data and real image reconstruction. Simulation results show that the new algorithm achieves a better performance than traditional approaches.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1948-9447
DOI:10.1109/CCDC.2016.7531304