Logistic tensor regression for classification

Authors:
Xu Tan;Yin Zhang;Siliang Tang;Jian Shao;Fei Wu;Yueting Zhuang
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

Logistic regression is one of the classical approaches for classification which has been widely used in computer vision, bioinformatics as well as multimedia understanding. However, when it is applied to high-dimensional data with structural information such as facial images or motion data, traditional vector-based logistic regression suffers from two main weaknesses: one is its negligence of structural information, and the other is its trend of overfitting. In this paper, we propose Logistic Tensor Regression (LTR) for classification of high-dimensional data with structural information. The proposed LTR not only reserves the underlying structural information embedded in data by tensorial representations, but also avoids overfitting by the introduction of a sparsity regularizer. Experiments on classification of facial images and motion data show that our proposed Logistic Tensor Regression approach outperforms the state-of-the-art algorithms.