Kernel Regularized Nonlinear Dictionary Learning for Sparse Coding
| Autor: | Bin Fang, Huaping Liu, He Liu, Fuchun Sun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
K-SVD
Computer science business.industry Iterative method 020207 software engineering Pattern recognition 02 engineering and technology Computer Science Applications Human-Computer Interaction Kernel (linear algebra) Control and Systems Engineering String kernel Kernel embedding of distributions Polynomial kernel Kernel (statistics) Radial basis function kernel 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering Tree kernel Neural coding business Software |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 49:766-775 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2017.2736248 |
| Popis: | For most sparse coding methods, data samples are first encoded as hand-crafted features, followed by another separate learning step that generates dictionary and sparse codes. However, such feature representations may not be optimally compatible with the learning process, thus producing suboptimal results. In this paper, we propose a new architecture for nonlinear dictionary learning with sparse coding, in which samples are mapped into sparse codes via carefully designed stacked auto-encoder (SAE) networks. We jointly learn a low-dimensional embedding of the data samples by means of an SAE and a dictionary in the low-dimensional space. Further, to leverage the prior knowledge, we develop a kernel regularized nonlinear dictionary learning method, which effectively incorporates the knowledge provided by the hand-crafted kernel. An iterative algorithm is developed to jointly search the solutions of the associated optimization problem and extensive experimental validations are performed to show that the proposed kernel regularized dictionary learning method achieves satisfactory performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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