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

• Zhonghua Liu,
• Gang Liu,
• Jiexin Pu,
• Shigang Liu,

Vol. 11, No. 5, May 30, 2017
10.3837/tiis.2017.05.012, Download Paper (Free):

• Noisy label
• discriminative least squares regression (DLSR)
• noisy label based discriminative least squares regression algorithm (NLDLSR)
• pattern recognition

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.

Statistics

Cite this article