JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 458
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306632
Title: Leveraging Hadoop for Escaping the Local Maxima Traps in EM Algorithms
Author(s): Putra Manggala*+
Address: 4454 Rue De Bullion, Montreal, QC, H2W2G2, Canada
Keywords: hadoop ; expectation-maximization ; parallel computing ; global maximum

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.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.