Difference between revisions of "Koushanfar2010"
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|abstract=<p>We introduce a unified approach for calculating nonparametric shape constrained regression. Enforcement of the shape constraint often accounts for the impact of a physical phenomenon or a specific property. It also improves the model\&$\#$39;s predicability and facilitates subsequent optimizations. The regression models are built by transforming the problem into the combinatorial domain where the shape constraints are imposed by bounding the combinatorial search space. We start by addressing isotonicity shape constraint using a dynamic programming algorithm and demonstrate how the problem can be mapped to the graph combinatorics domain. Next we show how a number of other important shape constraints including unimodality, convexity, limited level set, and limited slope can be addressed using the same framework. The flexibility of proposed framework enables solving the shape constrained regression problem with an arbitrary user-defined error metric. This flexibility is exploited to add robustness against outliers to the model. The algorithms are described in detail and their computational complexity is established. The performance and effectiveness of the shape constrained regression is evaluated on traces of temperature and humidity measurements from a deployed sensor network where a high degree of accuracy and robustness is demonstrated.</p> | |abstract=<p>We introduce a unified approach for calculating nonparametric shape constrained regression. Enforcement of the shape constraint often accounts for the impact of a physical phenomenon or a specific property. It also improves the model\&$\#$39;s predicability and facilitates subsequent optimizations. The regression models are built by transforming the problem into the combinatorial domain where the shape constraints are imposed by bounding the combinatorial search space. We start by addressing isotonicity shape constraint using a dynamic programming algorithm and demonstrate how the problem can be mapped to the graph combinatorics domain. Next we show how a number of other important shape constraints including unimodality, convexity, limited level set, and limited slope can be addressed using the same framework. The flexibility of proposed framework enables solving the shape constrained regression problem with an arbitrary user-defined error metric. This flexibility is exploited to add robustness against outliers to the model. The algorithms are described in detail and their computational complexity is established. The performance and effectiveness of the shape constrained regression is evaluated on traces of temperature and humidity measurements from a deployed sensor network where a high degree of accuracy and robustness is demonstrated.</p> | ||
|pages=626 - 637 | |pages=626 - 637 | ||
|month=2 | |||
|year=2010 | |||
|volume=58 | |volume=58 | ||
|journal=IEEE Trans. On Signal Processing | |journal=IEEE Trans. On Signal Processing | ||
|title=Nonparametric Combinatorial Regression for Shape Constrained Modeling | |title=Nonparametric Combinatorial Regression for Shape Constrained Modeling | ||
|entry=article | |entry=article | ||
}} | }} | ||
Revision as of 03:35, 4 September 2021
| Koushanfar2010 | |
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| author | Farinaz Koushanfar and Mehrdad Majzoobi and Potkonjak, Miodrag |
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| journal | IEEE Trans. On Signal Processing |
| month | 2 |
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| pages | 626 - 637 |
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| title | Nonparametric Combinatorial Regression for Shape Constrained Modeling |
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| volume | 58 |
| year | 2010 |
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| url | http://dl.acm.org/citation.cfm?id=1737518 |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093