A Constrained Algorithm Based NMF α for Image Representation

Chenxue Yang, Tao Li, Mao Ye, Zijian Liu and Jiao Bao

Discrete Dynamics in Nature and Society, 2014, vol. 2014, 1-12

Abstract:

Nonnegative matrix factorization (NMF) is a useful tool in learning a basic representation of image data. However, its performance and applicability in real scenarios are limited because of the lack of image information. In this paper, we propose a constrained matrix decomposition algorithm for image representation which contains parameters associated with the characteristics of image data sets. Particularly, we impose label information as additional hard constraints to the α -divergence-NMF unsupervised learning algorithm. The resulted algorithm is derived by using Karush-Kuhn-Tucker (KKT) conditions as well as the projected gradient and its monotonic local convergence is proved by using auxiliary functions. In addition, we provide a method to select the parameters to our semisupervised matrix decomposition algorithm in the experiment. Compared with the state-of-the-art approaches, our method with the parameters has the best classification accuracy on three image data sets.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:179129

DOI: 10.1155/2014/179129

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