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13 septembre 2015


Filed under: Uncategorized — pouc @ 2:04

C’est pas un statisticien ce gars là !

[Chopra et al., 2007]: Discovering the hidden structure of house prices with non-parametric latent manifold model (KDD 2007): 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 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 intrinsic price and desirability. The two components are trained simultanesously using a deterministic form of the EM algorithm. The model was trained on a large dataset of house prices 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.


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