International Journal of Applied Mathematics and Machine LearningAuthor's: Huan Xu, Wenjing Yan, Gang Zhu, Yinghao Deng and Ruonan Zhang
Pages: [1] - [12]
Received Date: August 24, 2019
Submitted by: Jianqiang Gao.
DOI: http://dx.doi.org/10.18642/ijamml_7100122097
Raw data from real-world applications are usually high-dimensional data with noise and redundancy information. How to obtain low-dimensional features of raw data is crucial to pattern recognition. In this paper, we propose a simple and effective discriminant feature construction (DFC) method, which exploits class labels and nonlinear similarity information to directly construct discriminant similarity features of training samples. The features keep the discriminating power of class labels and similarity information as much as possible. Extensive experiments on several real-world image datasets have demonstrated the superior performance of our proposed method.
feature construction, discriminant similarity feature, image recognition.
