Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion
- Resource Type
- Authors
- Conrad Beyers; Joel Janek Dabrowski; Johan P.R. de Villiers
- Source
- Information Fusion. 42:75-101
- Subject
- 0209 industrial biotechnology
Computer science
business.industry
Inference
Pattern recognition
02 engineering and technology
Linear dynamical system
Naive Bayes classifier
020901 industrial engineering & automation
Hardware and Architecture
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Fuse (electrical)
Key (cryptography)
020201 artificial intelligence & image processing
Artificial intelligence
Hidden Markov model
business
Software
Smoothing
Information Systems
- Language
- ISSN
- 1566-2535
The Naive Bayes Switching Linear Dynamical System (NB-SLDS) is proposed as a novel variant of the switching linear dynamical system (SLDS). The variant models multi-variable systems that undergo regime changes in their dynamics. The model may be applied to identify regime changes or classify systems according to their dynamics. The NB-SLDS provides the means to fuse multiple sequential data sources into a single model. A key feature of the model is that it is able to handle missing and unsynchronised data. Filtering and smoothing algorithms for inference and an expectation maximisation algorithm for parameter learning in the NB-SLDS are presented. The model is demonstrated and compared to the SLDS and hidden Markov model (HMM) in a human action recognition problem.