Traditional linear dimension reduction methods such as Principal component analysis (PCA) and linear discriminant analysis (LDA) have been used in many application areas due to simplicity and high performance. However, in data streams where data instances are generated continuously over time, it is difficult to apply traditional PCA or LDA. Moreover, data streams can have drifting concepts over time. In this paper, we compared several incremental linear dimension reduction algorithms which can be applied for classification in streaming data. Also, the performance comparison for prediction accuracy and time complexity was conducted in various streaming environments such as low dimensional data streams, high dimensional data streams, and data streams with concept drifts. Experimental results showed that incremental least squares formulation (ILS) combined with incremental PCA can be used effectively for classification in streaming data.

由于简单和高性能，传统的线性降维方法（例如主成分分析（PCA）和线性判别分析（LDA））已在许多应用领域中使用。但是，在随时间连续生成数据实例的数据流中，很难应用传统的PCA或LDA。此外，数据流可能会随着时间的流逝而发生漂移。在本文中，我们比较了几种可用于流数据分类的增量线性降维算法。此外，在各种流环境中（例如低维数据流，高维数据流和具有概念漂移的数据流）进行了预测准确性和时间复杂度的性能比较。