2D Multi-view Discriminant Analysis

Abstract

With the advent of technology, we have seen an explosion in high-definition images. However, due to the large size of images, 1D methods are less suitable to handle such data. This issue becomes even more pronounced when working with multi-view datasets. We propose a generalized method, 2D Multi-view Discriminant Analysis (2DMvDA), to tackle this issue. 2DMvDA is a 2D multi-view classification method based on discriminant analysis. It uses the 2D image matrices directly instead of extracting 1D features from them, leading to a considerable reduction in the size of the data to be processed. We compare the proposed method with 1D Multi-view Discriminant Analysis (MvDA) and single-view 2D Linear Discriminant Analysis (2DLDA). We experimentally show that the proposed method requires less than 0.1% training time and memory than MvDA. Also, though it needs the same memory, it requires 30–70% less training time than 2DLDA. Using less time and memory, 2DMvDA achieves a classification accuracy that is at par or better than both of these methods.