Abstract
Learning with streaming data has received extensive attention during the past few years. Existing approaches assume the feature space is fixed
or changes by following explicit regularities, limiting their applicability in dynamic environments
where the data streams are described by an arbitrarily varying feature space. To handle such
capricious data streams, we in this paper develop
a novel algorithm, named OCDS (Online learning from Capricious Data Streams), which does
not make any assumption on feature space dynamics. OCDS trains a learner on a universal feature
space that establishes relationships between old and
new features, so that the patterns learned in the
old feature space can be used in the new feature
space. Specifically, the universal feature space is
constructed by leveraging the relatednesses among
features. We propose a generative graphical model
to model the construction process, and show that
learning from the universal feature space can effectively improve the performance with theoretical
analysis. The experimental results demonstrate that
OCDS achieves conspicuous performance on both
synthetic and real datasets