Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis

In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical m...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 38; H. 7; S. 1356 - 1369
Hauptverfasser: Bao, Chenglong, Ji, Hui, Quan, Yuhui, Shen, Zuowei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Convergence
convergence analysis
Dictionaries
dictionary learning
Encoding
Image coding
Learning systems
non-convex optimization
Numerical analysis
Optimization
sparse coding
non-convex optimization
convergence analysis
sparse coding
Dictionary learning
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung:In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we propose an alternating iteration scheme for solving such problems. A rigorous convergence analysis shows that the proposed method satisfies the global convergence property: the whole sequence of iterates is convergent and converges to a critical point. Besides the theoretical soundness, the practical benefit of the proposed method is validated in applications including image restoration and recognition. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2015.2487966