Model Selection in Clustering by Uniform Convergence Bounds
Abstract
Unsupervised learning algorithms are designed to extract struc(cid:173) ture from data samples. Reliable and robust inference requires a guarantee that extracted structures are typical for the data source, Le., similar structures have to be inferred from a second sample set of the same data source. The overfitting phenomenon in max(cid:173) imum entropy based annealing algorithms is exemplarily studied for a class of histogram clustering models. Bernstein's inequality for large deviations is used to determine the maximally achievable approximation quality parameterized by a minimal temperature. Monte Carlo simulations support the proposed model selection cri(cid:173) terion by finite temperature annealing.
Cite
Text
Buhmann and Held. "Model Selection in Clustering by Uniform Convergence Bounds." Neural Information Processing Systems, 1999.Markdown
[Buhmann and Held. "Model Selection in Clustering by Uniform Convergence Bounds." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/buhmann1999neurips-model/)BibTeX
@inproceedings{buhmann1999neurips-model,
title = {{Model Selection in Clustering by Uniform Convergence Bounds}},
author = {Buhmann, Joachim M. and Held, Marcus},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {216-222},
url = {https://mlanthology.org/neurips/1999/buhmann1999neurips-model/}
}