The capacity of a clustering model can be defined as the ability to represent complex spatial data distributions. We introduce a method to quantify the capacity of an approximate spectral clustering model based on the eigenspectrum of the similarity matrix, providing the ability to measure capacity in a direct way and to estimate the most suitable model parameters. The method is tested on simple datasets and applied to a forged banknote classification problem.
Titolo: | Measuring clustering model complexity |
Autori: | |
Data di pubblicazione: | 2017 |
Serie: | |
Handle: | http://hdl.handle.net/11567/885687 |
ISBN: | 9783319686110 |
Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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