Supervised Dimensionality Reduction on Streaming Data

We propose a sliding-window approach for the dimensionality reduction for linear discriminant analysis(LDA) on streaming data. Streaming data are time variant and can be in high dimensions. When a sliding window is moving along data stream, the data that have passed out of the window will be forgotten (i.e., deleted).We propose a LDA dimensionality reduction algorithm based on different sliding windows. The experiments on UCI data sets have been conducted and results are compared with the batch IDR/QR LDA method. It is shown that our algorithm present an efficient solution to the problem of dimensionality reduction on streaming data yet still have a good performance on computational cost and the classification accuracy.