Skip navigation
Skip navigation
Open Research will be down for maintenance between 8:00 and 8:15 am on Tuesday, December 1 2020.

Regularized principal manifolds

Smola, Alexander; Mika, Sebastian; Schoelkopf, Bernhard; Williamson, Robert


Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised learning. This setting turns out to be closely related to principal curves, the generative topographic map, and robust coding. We explore this connection in two ways: (1) we propose an algorithm for nding...[Show more]

CollectionsANU Research Publications
Date published: 2001-06
Type: Journal article
Source: Journal of Machine Learning Research
DOI: 10.1162/15324430152748227


File Description SizeFormat Image
Smola_Regularized2001.pdf711.87 kBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator