{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/99847"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/99847","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Approximate k-means clustering through random projections","abstract":"Using random row projections, we show how to approximate a data matrix A with a much smaller sketch à that can be used to solve a general class of constrained k-rank approximation problems to within (1 + [epsilon]) error. Importantly, this class of problems includes k-means clustering. By reducing data points to just O(k) dimensions, our methods generically accelerate any exact, approximate, or heuristic algorithm for these ubiquitous problems. For k-means dimensionality reduction, we provide (1+ [epsilon]) relative error results for random row projections which improve on the (2 + [epsilon]) prior known constant factor approximation associated with this sketching technique, while preserving the number of dimensions. For k-means clustering, we show how to achieve a (9 + [epsilon]) approximation by Johnson-Lindenstrauss projecting data points to just 0(log k/[epsilon]2 ) dimensions. This gives the first result that leverages the specific structure of k-means to achieve dimension independent of input size and sublinear in k.","abstract_html":"Using random row projections, we show how to approximate a data matrix A with a much smaller sketch à that can be used to solve a general class of constrained k-rank approximation problems to within (1 + [epsilon]) error. Importantly, this class of problems includes k-means clustering. 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