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Abstract:
In this paper, the technique of stacking, previously only
used for supervised learning, is applied to unsupervised learning.
Specifically, it is used for non-parametric multivariate density
estimation, to combine finite mixture model and kernel density
estimators. Experimental results on both simulated data and real
world data sets clearly demonstrates 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 single best model chosen by "cheating" by
looking at the data used for independent testing.
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