A Sparse Regression Mixture Model for Clustering Time-Series
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Simultaneous curve registration and clustering for functional data
Computational Statistics & Data Analysis
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
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Clustering and prediction of sets of curves is an important problem in many areas of science and engineering. Most clustering algorithms operate on fixed-dimensional feature vectors, and as a result, curve analysis is often forced into this unnatural paradigm. Perhaps more importantly, curves tend to be misaligned from each other in a continuous manner, either in space (across the measurements) or in time. However, the notion of time within a feature-vector is very rigid corresponding only to the discrete dimensional setup of the space itself. In contrast to this, we develop a probabilistic framework that allows for the joint clustering and continuous alignment of sets of curves in curve space. Our proposed methodology integrates new probabilistic alignment models with model-based curve clustering algorithms. The probabilistic approach allows for the derivation of consistent EM-type learning algorithms for the joint clustering-alignment problem. Both simulated and real-world datasets are used for detailed experimentation, with two extensive applications to the clustering of cyclone trajectories presented.