Model Based Bayesian Curve Compression.

In this work, the curve compression problem is approached with a model-based probabilistic framework. We propose three different models. The proposed models can be used for purposes such as feature extraction or compression. The first model we propose is basically a Bayesian regression model for fitting piece-wise defined segments. The second model unifies clustering with regression. The third model combines Hidden Markov Models with regression via adding temporal connectivity to the second model. Since these models unify the mentioned paradigms, we believe that this work may be interesting from the Bayesian modeling perspective, besides the usefulness of the proposed models for curve compression applications.