Online Learning Under Capricious Feature Data Streams

Abstract

Online learning is advantageous for its efficiency and effectiveness in handling ever-growing data. Most existing methods assume that the features are fixed, but they can keep varying in such a way that old ones vanish and new ones emerge. To address these capricious features, this study proposes a subspace learning method. Specifically, a devised subspace estimator maps heterogeneous feature instances to a low-dimensional subspace and then a classifier is learned in this latent subspace. The estimator and the classifier are obtained recursively via alternating updating to sketch data in an online fashion. Under some mild assumptions, we provide its theoretical performance guarantee. The experimental results on several datasets corroborate the rationality of the theoretical analysis and the effectiveness of this novel scheme.