Principal manifold learning by sparse grids

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Title:Main Title: Principal manifold learning by sparse grids
Description: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:10.1007/s00607-009-0045-8 (DOI)
Responsible Party
Creators:Christian Feuersänger (Author), Michael Griebel (Author)
Publisher:Springer
Publication Year:2013
Topic
TR32 Topic:Other
Related Subproject:D5
Subjects:Keywords: Sparse Grids, Regularized Principal Manifolds, High-Dimensional Data
File Details
Filename:2009_Feuersaenger_Computing.pdf
Data Type:Text - Article
Size:33 Pages
File Size:5.7 MB
Dates:Accepted: 28.04.2009
Issued: 28.07.2009
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
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Download Permission:Only Project Members
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:[TR32DB] Data policy agreement
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Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Computing
Volume:85
Number of Pages:33 (267 - 299)
Metadata Details
Metadata Creator:Harrie-Jan Hendricks-Franssen
Metadata Created:03.12.2013
Metadata Last Updated:03.12.2013
Subproject:D5
Funding Phase:1
Metadata Language:English
Metadata Version:V50
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