Multi-view Incremental Discriminant Analysis

Abstract

In recent years, the use of multi-view data has attracted much attention resulting in many multi-view batch learning algorithms. However, these algorithms prove expensive in terms of training time and memory when used on the incremental data. In this paper, we propose Multi-view Incremental Discriminant Analysis (MvIDA), which updates the trained model to incorporate new data samples. MvIDA requires only the old model and newly added data to update the model. Depending on the nature of the increments, MvIDA is presented as two cases, sequential MvIDA and chunk MvIDA. We have compared the proposed method against the batch Multi-view Discriminant Analysis (MvDA) for its discriminability, order independence, the effect of the number of views, training time, and memory requirements. We have also compared our method with single-view Incremental Linear Discriminant Analysis (ILDA) for accuracy and training time. The experiments are conducted on four datasets with a wide range of dimensions per view. The results show that through order independence and faster construction of the optimal discriminant subspace, MvIDA addresses the issues faced by the batch multi-view algorithms in the incremental setting.