Presentation	2007-12-22 Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy Yuta ISHIKAWA, Ryohei NAKANO,
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Abstract(in English)	The EM algorithm is an efficient algorithm to obtain the ML estimate for incomplete data, but has the local optimality problem. The deterministic annealing EM (DAEM) algorithm was once proposed to solve this problem, which begins a search from the primitive initial point. Then the mes-EM algorithm was proposed: a variant of the m-EM algorithm which begins the multiple-token EM search from the primitive initial point. The mes-EM could obtain excellent solutions in compensation for rather high computing cost. This paper proposes a lighter version of the mes-EM algorithm using the subsampling strategy and evaluates its performance.
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Keyword(in English)	EM algorithm / Gaussian mixture estimation / sampling and subsampling strategy / primitive initial point
Paper #	NC2007-72
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Conference Information
Committee	NC
Conference Date	2007/12/15(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy
Sub Title (in English)
Keyword(1)	EM algorithm
Keyword(2)	Gaussian mixture estimation
Keyword(3)	sampling and subsampling strategy
Keyword(4)	primitive initial point
1st Author's Name	Yuta ISHIKAWA
1st Author's Affiliation	Nagoya Institute of Technology()
2nd Author's Name	Ryohei NAKANO
2nd Author's Affiliation	Nagoya Institute of Technology
Date	2007-12-22
Paper #	NC2007-72
Volume (vol)	vol.107
Number (no)	410
Page	pp.pp.-
#Pages	6
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