  Concept drift in learning and classification occurs when the statistical
properties of either the data features or target change over time; evidence of
drift has appeared in search data, medical research, malware, web data, and
video. Drift adaptation has not yet been addressed in high dimensional, noisy,
low-context data such as streaming text, video, or images due to the unique
challenges these domains present. We present a two-fold approach to deal with
concept drift in these domains: a density-based clustering approach to deal
with virtual concept drift (change in statistical properties of features) and a
weak-supervision step to deal with real concept drift (change in statistical
properties of target). Our density-based clustering avoids problems posed by
the curse of dimensionality to create an evolving 'map' of the live data space,
thereby addressing virtual drift in features. Our weak-supervision step
leverages high-confidence labels (oracle or heuristic labels) to generate
weighted training sets to generalize and update existing deep learners to adapt
to changing decision boundaries (real drift) and create new deep learners for
unseen regions of the data space. Our results show that our two-fold approach
performs well with >90% precision in 2018, four years after initial deployment
in 2014, without any human intervention.
