SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Diogo Pernes (Autor) FEUP; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the International Joint Conference on Neural Networks

Páginas: 1-10

2019 International Joint Conference on Neural Networks, IJCNN 2019

14 July 2019 through 19 July 2019

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-57Y

DOI: 10.1109/ijcnn.2019.8851929

Abstract (EN): We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10