A Gradient-Based Algorithm Competitive with Variational Bayesian EM for Mixture of Gaussians

被引：0

作者：

Kuusela, Mikael ^{[1
]}

Raiko, Tapani ^{[1
]}

Honkela, Antti ^{[1
]}

Karhunen, Juha ^{[1
]}

机构：

[1] Aalto Univ, Adapt Informat Res Ctr, Helsinki, Finland

来源：

IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6 | 2009年

关键词：

CONJUGATE-GRADIENT;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

While variational Bayesian (VB) inference is typically done with the so called VB EM algorithm, there are models where it cannot be applied because either the E-step or the M-step cannot be solved analytically. In 2007, Honkela et al. introduced a recipe for a gradient-based algorithm for VB inference that does not have such a restriction. In this paper, we derive the algorithm in the case of the mixture of Gaussians model. For the first time, the algorithm is experimentally compared to VB EM and its variant with both artificial and real data. We conclude that the algorithms are approximately as fast depending on the problem.

引用

页码：1011 / 1018

页数：8

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