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Structure regularized sparse coding for data representation
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DOI：10.1016/j.knosys.2019.02.035.png)
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Abstract
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Sparse coding (SC) exhibits impressive performance in many practical applications. However, in the unsupervised scenario, most of the conventional SC methods fail to fully take advantage of the structure of the data. Actually, the structure of the data, especially the global structure that is an implicit prior knowledge, is vital for data analysis. In this paper, we propose a novel method called structure regularized sparse coding (SRSC) for the sparse representation of the data in the unsupervised scenario. In contrast with the other SC methods, a distinct feature of SRSC is that it takes into consideration both the local and global structure of the data and fully exploits the latent category information in the data. By using the local affinity matrix that captures the local structure, we first build a global affinity matrix to encode the global structure of the data. The global affinity matrix fully carries the latent category information that is beneficial to obtain the discriminating representation of the data, Then, we define the optimization model of SRSC and develop a two-step iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve it. The experimental results validate that the proposed method is effective and can achieve better performance over its counterparts. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Data representation
Sparse coding
Graph regularized
Unsupervised learning
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