Stacked Density Estimation

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Padhraic Smyth, David Wolpert


In this paper, the technique of stacking, previously only used for supervised learning, is applied to unsupervised learning. Specifi(cid:173) cally, it is used for non-parametric multivariate density estimation, to combine finite mixture model and kernel density estimators. Ex(cid:173) perimental results on both simulated data and real world data sets clearly demonstrate that stacked density estimation outperforms other strategies such as choosing the single best model based on cross-validation, combining with uniform weights, and even the sin(cid:173) gle best model chosen by "cheating" by looking at the data used for independent testing.