Name
Abstract | ||
|---|---|---|
Single index model is a powerful yet simple model, widely used in statistics, machine learning, and other scientific fields. It models the regression function as $g( )$, where a is an unknown index vector and x are the features. This paper deals with a nonlinear generalization of this framework to allow for a regressor that uses multiple index vectors, adapting to local changes in the responses. To do so we exploit the conditional distribution over function-driven partitions, and use linear regression to locally estimate index vectors. We then regress by applying a kNN type estimator that uses a localized proxy of the geodesic metric. We present theoretical guarantees for estimation of local index vectors and out-of-sample prediction, and demonstrate the performance of our method with experiments on synthetic and real-world data sets, comparing it with state-of-the-art methods. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Statistics Theory | Journal |
Volume | Citations | PageRank |
abs/1902.09024 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeljko Kereta | 1 | 0 | 0.68 |
| Timo Klock | 2 | 0 | 0.68 |
| V Naumova | 3 | 30 | 5.06 |