Paper Info
Title
A novel approach for modeling positive vectors with inverted Dirichlet-based hidden Markov models
Abstract
Hidden Markov Models (HMMs) are among the most remarkably powerful probabilistic models, that although been acknowledged for decades have recently made a huge resurgence in the machine learning field. Their ever-growing use to model diversified and heterogeneous data (image,video, audio, time series) in numerous important practical situations is the subject of all forms of perpetual extensions. This work presents what we believe to be the first integration of the Inverted Dirichlet (ID) Mixture Models into the framework of HMMs. The proposed method uses the inverted Dirichlet mixtures to model the emission probabilities also known as observation probabilities. This extension (IDHMM), is motivated by the proven capacity of these mixtures to deal with positive vectors and overcome mixture models’ capability to take into account any ordering or temporal constraints relative to information. The complete inference and parameter estimation are detailed in this work. Applications in the context of image categorization and indoor occupancy detection demonstrate higher performance compared to the extensively used Gaussian mixture-based Hidden Markov Model (GHMM) and the Dirichlet mixture-based hidden Markov Model (DHMM).
Year
DOI
Venue
2020
10.1016/j.knosys.2019.105335
Knowledge-Based Systems
Keywords
Field
DocType
Hidden Markov Models,Inverted Dirichlet,Texture categorization,Occupancy detection,Smart buildings,Facial expressions recognition
Categorization,Inference,Computer science,Gaussian,Artificial intelligence,Dirichlet distribution,Estimation theory,Probabilistic logic,Hidden Markov model,Machine learning,Mixture model
Journal
Volume
ISSN
Citations 
192
0950-7051
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Rim Nasfi100.34
Manar Amayri204.39
Nizar Bouguila31539146.09