Algorithms in data mining using matrix and tensor methods

Algorithms in data mining using matrix and tensor methods

In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes. Data mining is a general concept involving different tools for performing this kind of analysis. The development of m...

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Bibliographic Details
Main Author: Savas, Berkant
Format: Doctoral Thesis
Language:English
Published: Linköpings universitet, Beräkningsvetenskap 2008
Subjects:
Volume
Minimization criterion
Determinant
Rank deficient matrix
Reduced rank regression
System identification
Rank reduction
Volume minimization
General algorithm
Handwritten digit classification
Tensors
Higher order singular value decomposition
Tensor approximation
Least squares
Tucker model
Multilinear algebra
Notation
Contraction
Tensor matricization
Newton's method
Grassmann manifolds
Product manifolds
Quasi-Newton algorithms
BFGS and L-BFGS
Symmetric tensor approximation
Local/intrinsic coordinates
Global/embedded coordinates;
Numerical analysis
Numerisk analys
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11597
http://nbn-resolving.de/urn:isbn:978-91-7393-907-2

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11597
http://nbn-resolving.de/urn:isbn:978-91-7393-907-2

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