Visual learning given sparse data of unknown complexity

Abstract

This study addresses the problem of unsupervised visual learning. It examines existing popular model order selection criteria before proposes two novel criteria for improving visual learning given sparse data and without any knowledge about model complexity. In particular, a rectified Bayesian information criterion (BICr) and a completed likelihood Akaike's… (More)
DOI: 10.1109/ICCV.2005.250

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Cite this paper

@article{Xiang2005VisualLG, title={Visual learning given sparse data of unknown complexity}, author={Tao Xiang and Shaogang Gong}, journal={Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1}, year={2005}, volume={1}, pages={701-708 Vol. 1} }