Normalized discriminant analysis for dimensionality reduction

作者：Liang Zhizheng; Xia Shixiong; Zhou Yong

来源：Neurocomputing, 2013, 110: 153-159.

DOI：10.1016/j.neucom.2012.12.007

• Dimensionality reduction
• Normalized Laplacain matrix
• Locality preserving projections (LPP)
• Normalized discriminant analysis (NDA)
• Data classification

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.

• 出版日期2013-6-13