An empirical comparison of incremental linear feature extraction methods

Abstract

Incremental feature extraction methods are effective in facilitating analysis of instance extensive applications. However, most current incremental feature extraction methods are not suitable for processing large-scale data with high feature dimension, since few methods have low time complexity. In recent years, some highly efficient incremental linear feature extraction methods were proposed whose time complexities are linear with both the numbers of instances and features, such as Incremental Principal Component Analysis IPCA, Incremental Maximum Margin Criterion IMMC and Incremental Inter-class Scatter IIS. Nevertheless, the performances of these incremental methods have not been compared directly yet. This paper proposes a novel comparative study of incremental feature extraction methods. Extensive experiments on handwritten digit recognition data set demonstrate the performances of the compared methods. Based on the extensive experimental results, the method of IMMC has been found to be the best among the compared feature extraction models.