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Kernel Regularized Nonlinear Dictionary Learning for Sparse Coding
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DOI：10.1109/TSMC.2017.2736248.png)
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摘要
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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 non-linear 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.
Keyword:
Kernel dictionary learning
kernel sparse coding
stacked auto-encoder (SAE)
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.8W
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