Title :
Analysis SimCO: A new algorithm for analysis dictionary learning
Author :
Jing Dong ; Wenwu Wang ; Wei Dai
Author_Institution :
Centre for Vision, Speech & Signal Process., Univ. of Surrey, Guildford, UK
Abstract :
We consider the dictionary learning problem for the analysis model based sparse representation. A novel algorithm is proposed by adapting the synthesis model based simultaneous codeword optimisation (SimCO) algorithm to the analysis model. This algorithm assumes that the analysis dictionary contains unit Ł2-norm atoms and trains the dictionary by the optimisation on manifolds. This framework allows one to update multiple dictionary atoms in each iteration, leading to a computationally efficient optimisation process. We demonstrate the competitive performance of the proposed algorithm using experiments on both synthetic and real data, as compared with three baseline algorithms, Analysis K-SVD, analysis operator learning (AOL) and learning overcomplete sparsifying transforms (LOST), respectively.
Keywords :
gradient methods; learning (artificial intelligence); optimisation; signal processing; analysis K-SVD; analysis dictionary learning; analysis model based sparse representation; analysis operator learning; learning overcomplete sparsifying transforms; simultaneous codeword optimisation algorithm; Algorithm design and analysis; Analytical models; Dictionaries; Manifolds; Optimization; Signal processing algorithms; Training; Analysis model; SimCO; analysis dictionary learning;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6854996