资源分类
》 资源 》论文》The variational hierarchical EM algorithm for clustering hidden Markov models

The variational hierarchical EM algorithm for clustering hidden Markov models

2020-01-13 | |  70 |   35 |   0

Abstract

In this paper, we derive a novel algorithm to cluster hidden Markov models (HMMs) according to their probability distributions. We propose a variational hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a “cluster center”, i.e., a novel HMM that is representative for the group. We illustrate the benefits of the proposed algorithm on hierarchical clustering of motion capture sequences as well as on automatic music tagging.

上一篇：Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction

下一篇：Multi-task Vector Field Learning

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...