Title: PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning. Abstract: Traditional machine learning is both data and computation intensive. The most powerful models require huge quantities of data to train and the training is highly time-consuming. In the streaming or incremental model of machine learning, the data is received and processed in a streaming manner, i.e., the entire data stream is not stored, and the models are updated incrementally. While this is closer to the learning process of humans, a common problem associated with this is “catastrophic forgetting” (CF), i.e., because the entire data is not stored, but just a sketch of it, as more and more data arrives, the older data has invariably a smaller representation in the stored sketch, and this causes models to perform badly on tasks that are closer to older data. One of the approaches to solve this problem stores an “exemplar set” of data items from the stream – but this raises the central question: how to choose which items to store? Current approaches
to solve this are based on herding, which is a way to select a random looking sample by a deterministic algorithm. We propose a novel selection approach based on Principal Component analysis and median sampling. This approach avoids the pitfalls due to outliers and is both simple to implement and use across various incremental machine learning models. It also has independent usage as a sampling algorithm. We achieve better performance compared to state-of-the-art methods.
