Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
State-space dynamics distance for clustering sequential data
Pattern Recognition
Using semi-parametric clustering applied to electronic health record time series data
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Unsupervised video surveillance
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
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Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering of vector inputs is straightforward, extensions to structured data or time-series data remain less explored. This paper proposes a clustering method for time-series data that couples non-parametric spectral clustering with parametric hidden Markov models (HMMs). HMMs add some beneficial structural and parametric assumptions such as Markov properties and hidden state variables which are useful for clustering. This article shows that using probabilistic pairwise kernel estimates between parametric models provides improved experimental results for unsupervised clustering and visualization of real and synthetic datasets. Results are compared with a fully parametric baseline method (a mixture of hidden Markov models) and a non-parametric baseline method (spectral clustering with non-parametric time-series kernels).