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Abstract Details
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Activity Number:
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458
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #306632 |
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Title:
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Leveraging Hadoop for Escaping the Local Maxima Traps in EM Algorithms
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Author(s):
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Putra Manggala*+
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Companies:
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Address:
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4454 Rue De Bullion, Montreal, QC, H2W2G2, Canada
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Keywords:
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hadoop ;
expectation-maximization ;
parallel computing ;
global maximum
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Abstract:
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Hadoop provides a framework for the analysis and transformation of very large data sets using the MapReduce paradigm, along with a distributed file system to store very large data sets reliably. There has been some applications of MapReduce for the parallelization of the EM algorithm, however we are keen on specifically tackling the local maxima trap problem. With respect to some of the current approaches, we propose to leverage Hadoop, both by its ability for parallelization in exploring multiple starting points in some schemes that are very amenable to the MapReduce framework. The main goal of these schemes is to efficiently explore large number of starting points while still performing reliable likelihood comparison, such that a global maximum is within reach.
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Authors who are presenting talks have a * after their name.
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