Incremental Canonical Correlation Analysis - Hongmin Zhao; Dongting Sun; Zhigang Luo (Applied Sciences, 2020)
Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.