Geometric Online Adaptation: Graph-Based OSFS for Streaming Samples

Feature selection seeks a curated subset of available features such that they contain sufficient discriminative information for a given learning task. Online streaming feature selection (OSFS) further extends this to the streaming scenario where the model gets only a single pass at features, one at a time. While this problem setting allows for training high performance models with low computational and storage requirements, this setting also makes the assumption that there is a fixed number of samples, which is often invalidated in many real-world problems. In this paper, we consider a new setting called Online Streaming Feature Selection with Streaming Samples (OSFS-SS) with a fixed class label space, where both the features and the samples are simultaneously streamed. We extend the state-of-the-art OSFS method to work in this setting. Furthermore, we introduce a novel algorithm, that has applications in both the OSFS and OSFS-SS settings, called Geometric Online Adaptation (GOA) which uses a graph-based class conditional geometric dependency (CGD) criterion to measure feature relevance and maintain a minimal feature subset with relatively high classification performance. We evaluate the proposed GOA algorithm on both simulation and real world datasets highlighting how in both the OSFS and OSFS-SS settings it achieves higher performance while maintaining smaller feature subsets than relevant baselines.