Troelsgaard Rasmus, Hansen Lars Kai
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby 2860, Denmark
Neural Comput. 2018 Jan;30(1):216-236. doi: 10.1162/neco_a_01033. Epub 2017 Nov 21.
Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.
使用一组隐马尔可夫模型对序列数据进行基于模型的分类是一项众所周知的技术。然而，所涉及的评分函数通常基于类条件似然，计算量可能很大，特别是对于长数据序列。受隐马尔可夫模型谱学习近期理论进展的启发，我们提出了一种基于三阶矩的评分函数。具体而言，我们建议使用理论三阶矩和经验三阶矩之间的库尔贝克-莱布勒散度对具有离散观测值的序列数据进行分类。与通常的基于似然的方法相比，所提出的方法在分类时提供了更低的计算复杂度。为了证明所提出方法的特性，我们对来自人类活动识别研究的模拟数据和经验数据都进行了分类。
