Letters: Normalized discriminant analysis for dimensionality reduction

Authors:
Zhizheng Liang;Shixiong Xia;Yong Zhou
Affiliations:
School of Computer Science and technology, China University of Mining and Technology, China;School of Computer Science and technology, China University of Mining and Technology, China;School of Computer Science and technology, China University of Mining and Technology, China
Venue:
Neurocomputing
Year:
2013

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Abstract

In this paper, we propose the normalized discriminant analysis (NDA) technique for dimensionality reduction. NDA is built on the information of data point pairs that is implicitly encoded by using the pseudo-Riemannian metric tensor. This makes NDA to be easily adapted for unsupervised or supervised learning. It is also interesting to note that the solution of NDA will asymptotically converge to that of generalized linear discriminant analysis (GLDA) under proper conditions. This gives us some insights in understanding the evolving behavior of NDA. Extensive experiments on a simulated data, face images, character images, and UCI data sets are carried out to demonstrate the effectiveness of NDA.