Timezone: »

 
Poster
Greedy Feature Construction
Dino Oglic · Thomas Gärtner

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #108
in Monday Posters »

We present an effective method for supervised feature construction. The main goal of the approach is to construct a feature representation for which a set of linear hypotheses is of sufficient capacity -- large enough to contain a satisfactory solution to the considered problem and small enough to allow good generalization from a small number of training examples. We achieve this goal with a greedy procedure that constructs features by empirically fitting squared error residuals. The proposed constructive procedure is consistent and can output a rich set of features. The effectiveness of the approach is evaluated empirically by fitting a linear ridge regression model in the constructed feature space and our empirical results indicate a superior performance of our approach over competing methods.

Keywords: Large Scale Learning and Big Data · Sparsity and Feature Selection

Author Information

Dino Oglic (University of Bonn)
Thomas Gärtner (The University of Nottingham)

More from the Same Authors