English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Exploring model selection techniques for nonlinear dimensionality reduction

Harmeling, S.(2007). Exploring model selection techniques for nonlinear dimensionality reduction (EDI-INF-RR-0960). Edinburgh, UK: School of Informatics, University of Edinburgh.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CE87-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-ED30-C
Genre: Report

Files

show Files
hide Files
:
EDI-INF-RR-0960.pdf (Publisher version), 4MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-0003-ED31-B
Name:
EDI-INF-RR-0960.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Harmeling, S1, Author           
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are faced with the analysis of high-dimensional data. Of course, not all NLDR methods are equally applicable to a particular dataset at hand. Thus it would be useful to come up with model selection criteria that help to choose among different NLDR algorithms. This paper explores various approaches to this problem and evaluates them on controlled data sets. Comprehensive experiments will show that model selection scores based on stability are not useful, while scores based on Gaussian processes are helpful for the NLDR problem.

Details

show
hide
Language(s):
 Dates: 2007-03
 Publication Status: Issued
 Pages: 20
 Publishing info: Edinburgh, UK : School of Informatics, University of Edinburgh
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: EDI-INF-RR-0960
BibTex Citekey: 6346
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show