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