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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
BEGIN:VEVENT
DTSTART:20151117T231500Z
DTEND:20151118T010000Z
LOCATION:Level 4 - Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: The data mining community voted Expectation Maximization (EM) algorithm as one of the top ten algorithms having the most impact on data mining research. EM is a popular iterative algorithm for learning mixture models with applications in various areas from computer vision, astronomy, to signal processing. We present a new high-performance parallel algorithm on multicore systems that impacts all stages of EM. We use tree data structures and user-controlled approximations to reduce the asymptotic runtime complexity of EM with significant performance improvements. PEAK utilizes the same tree and algorithmic framework for all the stages of EM. =0A=0AExperimental results show that our parallel algorithm significantly outperforms the state-of-the-art algorithms and libraries on all dataset configurations (varying number of points, dimensionality of the dataset, and number of mixtures). Looking forward, we identify approaches to extend this idea to a larger scale of similar problems.
SUMMARY:PEAK: Parallel EM Algorithm using Kd-tree
PRIORITY:3
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