An architecture for streaming coclustering in high speed hardware

We seek to learn the semantics of a data stream at optical line speed. We focus on text data, but the techniques developed should apply to broad modalities of network data wherever appropriate features can be computed rapidly enough. We consider a custom hardware system designed to categorize documents based on feature clusters and document clusters that have been learned offline on standard general-purpose computers, and we present a technique for extending this system to permit online learning from arbitrarily large data sets