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Discovering the hidden structure of house prices with a non-parametric manifold model (2007)
| Venue: | 13th International Conference on Knowledge Discovery and Data Mining (KDD |
| Citations: | 5 - 3 self |
BibTeX
@INPROCEEDINGS{Leahy07discoveringthe,
author = {John Leahy and Sumit Chopra and Andrew Caplin and Trivikraman Thampy and Yann Lecun},
title = {Discovering the hidden structure of house prices with a non-parametric manifold model},
booktitle = {13th International Conference on Knowledge Discovery and Data Mining (KDD},
year = {2007}
}
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Abstract
In many regression problems, the variable to be predicted depends not only on a sample-specific feature vector, but also on an unknown (latent) manifold that must satisfy known constraints. An example is house prices, which depend on the characteristics of the house, and on the desirability of the neighborhood, which is not directly measurable. The proposed method comprises two trainable components. The first one is a parametric model that predicts the “intrinsic ” price of the house from its description. The second one is a smooth, non-parametric model of the latent “desirability ” manifold. The predicted price of a house is the product of its intrinsic price and desirability. The two components are trained simultaneously using a deterministic form of the EM algorithm. The model was trained on a large dataset of houses from Los Angeles county. It produces better predictions than pure parametric and non-parametric models. It also produces useful estimates of the desirability surface at each location.
Keyphrases
house price hidden structure non-parametric manifold model intrinsic price non-parametric model sample-specific feature vector los angeles county useful estimate parametric model desirability surface predicted price second one first one trainable component deterministic form large dataset latent desirability manifold em algorithm many regression problem
