Ensemble-based local learning for high-dimensional data regression


In this paper we propose a new local learning based regression method which utilizes ensemble-learning as a form of regularization to reduce the variance of local estimators. This makes it possible to use local learning methods even with very high-dimensional datasets. The efficacy of the proposed method is illustrated on two publicly available high-dimensional sets in comparison with several global learning methods, and it is shown that the proposed ensemble-based local learning method significantly outperforms the global ones.


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