TR32-Database: Database of Transregio 32

[759] - Principal manifold learning by sparse grids

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Feuersänger, C., Griebel, M., 2009. Principal manifold learning by sparse grids. Computing, 85, 267 - 299. DOI: 10.1007/s00607-009-0045-8.
Identification
Title(s):Main Title: Principal manifold learning by sparse grids
Description(s):Abstract: In this paper, we deal with the construction of lower-dimensional manifolds from high-dimensional data which is an important task in data mining, machine learning and statistics. Here, we consider principal manifolds as the minimum of a regularized, non-linear empirical quantization error functional. For the discretization we use a sparse grid method in latent parameter space. This approach avoids, to some extent, the curse of dimension of conventional grids like in the GTM approach. The arising non-linear problem is solved by a descent method which resembles the expectation maximization algorithm.We present our sparse grid principal manifold approach, discuss its properties and report on the results of numerical experiments for one-, two and three-dimensional model problems.
Identifier(s):DOI: 10.1007/s00607-009-0045-8
Responsible Party
Creator(s):Author: Christian Feuersänger
Author: Michael Griebel
Publisher:Springer
Topic
TR32 Topic:Other
Subject(s):CRC/TR32 Keywords: Sparse Grids, Regularized Principal Manifolds, High-Dimensional Data
File Details
File Name:2009_Feuersaenger_Computing.pdf
Data Type:Text
Size(s):33 Pages
File Size:5822 kB (5.686 MB)
Date(s):Date Accepted: 2009-04-28
Issued: 2009-07-28
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
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Measurement Region:None
Measurement Location:--None--
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2009
Type:Article
Article Type:Journal
Source:Computing
Volume:85
Number Of Pages:33
Page Range:267 - 299
Metadata Details
Metadata Creator:Harrie-Jan Hendricks-Franssen
Metadata Created:2013-12-03
Metadata Last Updated:2013-12-03
Subproject:D5
Funding Phase:1
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:144
Metadata Downloads:0
Dataset Downloads:0
Dataset Activity
Features