• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Noisy label based discriminative least squares regression and its kernel extension for object identification


Abstract

In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.


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Cite this article

[IEEE Style]
Zhonghua Liu, Gang Liu, Jiexin Pu and Shigang Liu, "Noisy label based discriminative least squares regression and its kernel extension for object identification," KSII Transactions on Internet and Information Systems, vol. 11, no. 5, pp. 2523-2538, 2017. DOI: 10.3837/tiis.2017.05.012

[ACM Style]
Liu, Z., Liu, G., Pu, J., and Liu, S. 2017. Noisy label based discriminative least squares regression and its kernel extension for object identification. KSII Transactions on Internet and Information Systems, 11, 5, (2017), 2523-2538. DOI: 10.3837/tiis.2017.05.012