Fast Algorithm for Online Linear Discriminant Analysis

摘要

Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, new algo- rithms of online LDA are proposed in the present paper. In face identification task, it is experimentally shown that the new algo- rithms are about two times faster than the previously proposed algorithm in terms of the number of required examples, while the previous algorithm attain better final performance than the new algorithms after sufficient steps of learning.