Incremental Linear Discriminant Analysis for Classification of Data Streams

TLDR

The study proposes a constructive method to update a discriminant eigenspace for classification as new class‑containing data bursts are added. The authors develop an incremental linear discriminant analysis (ILDA) algorithm in sequential and chunked forms, evaluate it on datasets varying in class count and dimensionality, and compare its discriminability, speed, and memory usage to batch LDA. ILDA effectively evolves a discriminant eigenspace over fast, large data streams and yields superior discriminability in classification compared to other methods.

Abstract

This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.