关键词: 典型相关分析（CCA）, 数据降维, 增量学习, 多视图分类

Abstract:

For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars. As an improved canonical correlation analysis (CCA) method based on incremental learning, incremental canonical correlation analysis (ICCA) can effectively deal with the problem of dimensionality reduction of high-dimensional multi-view data stream. However, there is a drawback in this approach that the projection vector must be updated once for each new sample, which consumes a lot of time on the issue of online learning. Aiming at this problem, chunk incremental canonical correlation analysis (CICCA) is proposed in this paper. It can avoid the calculation of sample covariance matrices and process batch data stream directly. The main projection vector is updated each time with the newly added batch sample information, which is used to revise and update the projection vector of the previous step. Further, the other projection vectors are calculated in the orthogonal complement space of the projection vector. Therefore, data can be got from low-dimensional spaces. Experimental results show that the classification performance of CICCA is comparable to CCA and ICCA, but the training time is greatly reduced on synthetic dataset and real dataset.

Key words: canonical correlation analysis (CCA), dimensionality reduction, incremental learning, multi-view classification