A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension

摘要

Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods and has been widely used in many applications. In the last decades many LDA-based dimension reduction algorithms have been reported. Among these methods, orthogonal LDA (OLDA) is a famous one and several different implementations of OLDA have been proposed. In this paper, we propose a new and fast implementation of OLDA. Compared with the other OLDA implementations, our proposed implementation of OLDA is the fastest one when the dimensionality d is larger than the sample size n. Then, based on our proposed implementation of OLDA, we present an incremental OLDA algorithm which can accurately update the projection matrix of OLDA when new samples are added into the training set. The effectiveness of our proposed new OLDA algorithm and its incremental version are demonstrated by some real-world data sets.