Name
Abstract | ||
|---|---|---|
In this work, we approach the piecewise curve approximation problem with a model-based probabilistic framework. For this purpose, we propose three different models. These models can be used for feature extraction or compression. The first model is a variant of the Bayesian regression model where we can parametrically alter the design matrix. The second model approaches the piecewise curve approximation as a clustering problem. The third model adds temporal connectivity to the second model and combines Hidden Markov models with linear regression. We run the first and the third models on a curve which is used to rank existing algorithms and show that our approaches outperforms its rivals. We also run our models on several real-life curves to show their capabilities. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal Processing Conference | Bayes methods,approximation theory,curve fitting,data compression,feature extraction,hidden Markov models,matrix algebra,pattern clustering,regression analysis,Bayesian regression model,clustering problem,design matrix,feature compression,feature extraction,hidden Markov model,linear regression,model-based probabilistic framework,piecewise curve approximation,temporal connectivity,Bayesian modeling,Curve segment clustering,Hidden Markov Models,Piecewise curve approximation |
Field | DocType | ISSN |
Curve fitting,Pattern recognition,Bayesian linear regression,Algorithm,Approximation theory,Design matrix,Artificial intelligence,Probabilistic logic,Cluster analysis,Hidden Markov model,Piecewise,Mathematics | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-4673-1068-0 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Cem Siibakan | 1 | 0 | 0.34 |
| Biilent Sankur | 2 | 0 | 0.34 |