Weighted SimCO: A novel algorithm for dictionary update
Zhao, X, Zhou, G, Dai, W and Wang, W (2012) Weighted SimCO: A novel algorithm for dictionary update In: Sensor Signal Processing for Defence (SSPD 2012), 2012-09-25 - 2012-09-27, London.
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Algorithms aiming at solving dictionary learning problem usually involve iteratively performing two stage operations: sparse coding and dictionary update. In the dictionary update stage, codewords are updated based on a given sparsity pattern. In the ideal case where there is no noise and the true sparsity pattern is known a priori, dictionary update should produce a dictionary that precisely represent the training samples. However, we analytically show that benchmark algorithms, including MOD, K-SVD and regularized SimCO, could not always guarantee this property: they may fail to converge to a global minimum. The key behind the failure is the singularity in the objective function. To address this problem, we propose a weighted technique based on the SimCO optimization framework, hence the term weighted SimCO. Decompose the overall objective function as a sum of atomic functions. The crux of weighted SimCO is to apply weighting coefficients to atomic functions so that singular points are zeroed out. A second order method is implemented to solve the corresponding optimization problem. We numerically compare the proposed algorithm with the benchmark algorithms for noiseless and noisy scenarios. The empirical results demonstrate the significant improvement in the performance.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||10.1049/ic.2012.0116|
|Additional Information :||© The Institution of Engineering and Technology 2012. This paper is a postprint of a paper submitted to and accepted for publication in the IET Seminar Digest and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||06 Dec 2013 17:23|
|Last Modified :||09 Jun 2014 13:46|
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