Name
Title | ||
|---|---|---|
Discovering the hidden structure of house prices with a non-parametric latent manifold model |
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1281192.1281214 | KDD |
Keywords | Field | DocType |
non-parametric model,hidden structure,desirability surface,non-parametric latent manifold model,deterministic form,parametric model,intrinsic price,pure parametric,los angeles county,better prediction,em algorithm,house price,expectation maximization,satisfiability,structured prediction,feature vector | Econometrics,Feature vector,Parametric model,Expectation–maximization algorithm,Measure (mathematics),Structured prediction,Nonparametric statistics,Parametric statistics,Artificial intelligence,Machine learning,Mathematics,Manifold | Conference |
Citations | PageRank | References |
10 | 3.24 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sumit Chopra | 1 | 2835 | 181.37 |
| Trivikraman Thampy | 2 | 10 | 3.24 |
| John Leahy | 3 | 14 | 4.02 |
| Andrew Caplin | 4 | 10 | 3.24 |
| Yann LeCun | 5 | 26090 | 3771.21 |