Discovering the hidden structure of house prices with a non-parametric latent manifold model

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Discovering the hidden structure of house prices with a non-parametric latent manifold model

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Jose, California, USA

SESSION: Research track papers table of contents

Pages: 173 - 182

Year of Publication: 2007

ISBN:978-1-59593-609-7

Authors

Sumit Chopra	New York University
Trivikraman Thampy	New York University
John Leahy	New York University
Andrew Caplin	New York University
Yann LeCun	New York University

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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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.

REFERENCES

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