Linear Discriminant Analysis for Large-Scale Data: Application on Text and Image Data

Abstract

Linear Discriminant Analysis (LDA) is a technique which is frequently used to extract discriminative features that preserve the class separability. LDA involves matrices eigen decomposition which can be computationally expensive in both time and memory, in particular when the number of samples and the number of features are large. This is the case for text and image data sets where the dimension can reach in order of hundreds of thousands or more. In this paper, we propose an efficient algorithm Fast-LDA to handle large scale data for discriminant analysis. The proposed approach uses a feature extraction method based on random projection to reduce the dimensionality and then perform LDA in the reduced space. By reducing data dimension, we reduce the complexity of data analysis. The accuracy and the computational time of the proposed approach are provided for a wide variety of real image and text data sets. The results show the relevance of the proposed method compared to other methods.