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Given the high volume of content being generated online, it becomes necessary to employ automated techniques to separate out the documents belonging to novel topics from the background discussion, in a robust and scalable manner (with respect to the size of the document set). We present a solution to this challenge based on sparse coding, in which a stream of documents (where each document is modeled as an m-dimensional vector y) can be used to learn a dictionary matrix A of dimension m k, suchdoi:10.1147/jrd.2013.2247232 fatcat:gb533tvb6nfnxab4v3vr65frry