An introduction to variational methods for graphical models
Learning in graphical models
Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Bayesian parameter estimation via variational methods
Statistics and Computing
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Networks
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Bayesian mixture modelling
Neurocomputing
Violence content classification using audio features
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Variational Bayes for generalized autoregressive models
IEEE Transactions on Signal Processing
Signal Modeling and Classification Using a Robust Latent Space Model Based on Distributions
IEEE Transactions on Signal Processing
Mixture-Based Extension of the AR Model and its Recursive Bayesian Identification
IEEE Transactions on Signal Processing
Unsupervised Learning of Gaussian Mixtures Based on Variational Component Splitting
IEEE Transactions on Neural Networks
The infinite Student's t-mixture for robust modeling
Signal Processing
A reservoir-driven non-stationary hidden Markov model
Pattern Recognition
Robust offline topological map estimation using visual loop closures
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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The Student's-t hidden Markov model (SHMM) has been recently proposed as a robust to outliers form of conventional continuous density hidden Markov models, trained by means of the expectation-maximization algorithm. In this paper, we derive a tractable variational Bayesian inference algorithm for this model. Our innovative approach provides an efficient and more robust alternative to EM-based methods, tackling their singularity and overfitting proneness, while allowing for the automatic determination of the optimal model size without cross-validation. We highlight the superiority of the proposed model over the competition using synthetic and real data. We also demonstrate the merits of our methodology in applications from diverse research fields, such as human computer interaction, robotics and semantic audio analysis.