English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Quantization Functionals and Regularized Principal Manifolds

Smola, A., Mika, S., & Schölkopf, B.(1998). Quantization Functionals and Regularized Principal Manifolds (NC2-TR-1998-028). London, UK: University of London, Royal Holloway College, NeuroCOLT 2.

Item is

Files

show Files

Creators

show
hide
 Creators:
Smola, AJ, Author              
Mika, S, Author
Schölkopf, B1, Author              
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Many settings of unsupervised learning can be viewed as quantization problems, namely of minimizing the expected quantization error subject to some restrictions. This has the advantage that tools known from the theory of (supervised) risk minimization like regularization can be readily applied to unsupervised settings. Moreover, one may show that this setting is very closely related to both, principal curves with a length constraint and the generative topographic map. Experimental results demonstrate the feasibility of the proposed method. In a companion paper we show that uniform convergence bounds can be given for algorithms such as a modified variant of the principal curves problem.

Details

show
hide
Language(s):
 Dates: 1998-09
 Publication Status: Published in print
 Pages: 9
 Publishing info: London, UK : University of London, Royal Holloway College, NeuroCOLT 2
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 1869
Report Nr.: NC2-TR-1998-028
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show