Learning Hidden Markov Models Using Nonnegative Matrix Factorization

Cybenko G.; Crespi V.

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Learning Hidden Markov Models Using Nonnegative Matrix Factorization

机译：使用非负矩阵分解学习隐马尔可夫模型

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The Baum-Welch algorithm together with its derivatives and variations has been the main technique for learning hidden Markov models (HMMs) from observational data. We present an HMM learning algorithm based on the nonnegative matrix factorization (NMF) of higher order Markovian statistics that is structurally different from the Baum-Welch and its associated approaches. The described algorithm supports estimation of the number of recurrent states of an HMM and iterates the NMF algorithm to improve the learned HMM parameters. Numerical examples are provided as well.

机译：Baum-Welch算法及其派生和变化形式已成为从观测数据中学习隐马尔可夫模型（HMM）的主要技术。我们提出了一种基于高阶马尔可夫统计量的非负矩阵分解（NMF）的HMM学习算法，该算法在结构上不同于Baum-Welch及其相关方法。所描述的算法支持对HMM循环状态数量的估计，并迭代NMF算法以改善学习到的HMM参数。还提供了数值示例。

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来源
《Information Theory, IEEE Transactions on》 |2011年第6期|p.3963-3970|共8页
作者
Cybenko G.; Crespi V.;
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作者单位

Thayer School of Engineering, Dartmouth College, Hanover, NH, USA;

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原文格式 PDF
正文语种 eng
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关键词
Hidden Markov models (HMMs); machine learning; nonnegative matrix factorization (NMF);

机译：隐马尔可夫模型（HMM）;机器学习;负矩阵分解（NMF）;

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Learning Hidden Markov Models Using Nonnegative Matrix Factorization

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