Non-Iterative Two-Dimensional Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. Recently, LDA has been extended to two-dimensional LDA (2DLDA), which is an iterative algorithm for data in matrix representation. In this paper, we propose non-iterative algorithms for 2DLDA. Experimental results show that the non-iterative algorithms achieve competitive recognition rates with the iterative 2DLDA, while they are computationally more efficient than the iterative 2DLDA.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Kohei Inoue, Kiichi Urahama
• Keywords: Computer Vision, ICPR 2006, Iterative Algorithm, Non-iterative Algorithms, Two-dimensional Lda