Maximum Entropy Density Estimation with Incomplete Presence-Only Data

Bert Huang, Ansaf Salleb-Aouissi
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:240-247, 2009.

Abstract

We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the learning process. We provide analysis and examples of our algorithm under different settings of missing data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-huang09a, title = {Maximum Entropy Density Estimation with Incomplete Presence-Only Data}, author = {Huang, Bert and Salleb-Aouissi, Ansaf}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {240--247}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/huang09a/huang09a.pdf}, url = {https://proceedings.mlr.press/v5/huang09a.html}, abstract = {We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the learning process. We provide analysis and examples of our algorithm under different settings of missing data.} }
Endnote
%0 Conference Paper %T Maximum Entropy Density Estimation with Incomplete Presence-Only Data %A Bert Huang %A Ansaf Salleb-Aouissi %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-huang09a %I PMLR %P 240--247 %U https://proceedings.mlr.press/v5/huang09a.html %V 5 %X We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the learning process. We provide analysis and examples of our algorithm under different settings of missing data.
RIS
TY - CPAPER TI - Maximum Entropy Density Estimation with Incomplete Presence-Only Data AU - Bert Huang AU - Ansaf Salleb-Aouissi BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-huang09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 240 EP - 247 L1 - http://proceedings.mlr.press/v5/huang09a/huang09a.pdf UR - https://proceedings.mlr.press/v5/huang09a.html AB - We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the learning process. We provide analysis and examples of our algorithm under different settings of missing data. ER -
APA
Huang, B. & Salleb-Aouissi, A.. (2009). Maximum Entropy Density Estimation with Incomplete Presence-Only Data. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:240-247 Available from https://proceedings.mlr.press/v5/huang09a.html.

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