Spectral compression algorithms for the analysis of very large multivariate images

Keenan, Michael R

Title: Spectral compression algorithms for the analysis of very large multivariate images

Abstract

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Inventors:

Keenan, Michael R ^[1]

Albuquerque, NM

Issue Date:: 2007-10-16

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 918543

Patent Number(s):: 7283684

Application Number:: 10/772,548

Assignee:: Sandia Corporation (Albuquerque, NM)

DOE Contract Number:: AC04-94AL85000

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Keenan, Michael R. Spectral compression algorithms for the analysis of very large multivariate images.  United States: N. p., 2007. 
        Web.

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                    Keenan, Michael R. Spectral compression algorithms for the analysis of very large multivariate images.  United States.

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                    Keenan, Michael R. Tue .  
        "Spectral compression algorithms for the analysis of very large multivariate images".  United States.  https://www.osti.gov/servlets/purl/918543.

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@article{osti_918543,

  title        = {Spectral compression algorithms for the analysis of very large multivariate images},

  author       = {Keenan, Michael R},

  abstractNote = {A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2007},

  month        = {10}

}

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Works referenced in this record:

Improving the speed of multiway algorithms
journal, August 1998

Bro, Rasmus; Andersson, Claus A.
Chemometrics and Intelligent Laboratory Systems, Vol. 42, Issue 1-2
https://doi.org/10.1016/S0169-7439(98)00011-2

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool
journal, January 2003

Kotula, Paul G.; Keenan, Michael R.; Michael, Joseph R.
Microscopy and Microanalysis, Vol. 9, Issue 1
https://doi.org/10.1017/S1431927603030058

The kernel PCA algorithms for wide data. Part I: Theory and algorithms
journal, April 1997

Wu, W.; Massart, D. L.; de Jong, S.
Chemometrics and Intelligent Laboratory Systems, Vol. 36, Issue 2
https://doi.org/10.1016/S0169-7439(97)00010-5

Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution
journal, June 1998

Andrew, Jeremy J.; Hancewicz, Thomas M.
Applied Spectroscopy, Vol. 52, Issue 6
https://doi.org/10.1366/0003702981944526

Fast principal component analysis of large data sets
journal, November 2001

Vogt, F.; Tacke, M.
Chemometrics and Intelligent Laboratory Systems, Vol. 59, Issue 1-2
https://doi.org/10.1016/S0169-7439(01)00130-7

Speed improvement of multivariate algorithms by the method of postponed basis matrix multiplication
journal, June 1994

Alsberg, Bjørn K.; Kvalheim, Olav M.
Chemometrics and Intelligent Laboratory Systems, Vol. 24, Issue 1
https://doi.org/10.1016/0169-7439(94)00013-1

Relating two proposed methods for speedup of algorithms for fitting two- and three-way principal component and related multilinear models
journal, February 1997

Kiers, Henk A. L.; Harshman, Richard A.
Chemometrics and Intelligent Laboratory Systems, Vol. 36, Issue 1
https://doi.org/10.1016/S0169-7439(96)00074-3

Fast principal component analysis of large data sets based on information extraction
journal, January 2002

Vogt, F.; Tacke, M.
Journal of Chemometrics, Vol. 16, Issue 11
https://doi.org/10.1002/cem.751

Impact of Hierarchical Memory Systems On Linear Algebra Algorithm Design
journal, March 1988

Gallivan, Kyle; Jalby, William; Meier, Ulrike
The International Journal of Supercomputing Applications, Vol. 2, Issue 1
https://doi.org/10.1177/109434208800200103

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Title: Spectral compression algorithms for the analysis of very large multivariate images

Abstract

Citation Formats

Improving the speed of multiway algorithms journal, August 1998

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool journal, January 2003

The kernel PCA algorithms for wide data. Part I: Theory and algorithms journal, April 1997

Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution journal, June 1998

Fast principal component analysis of large data sets journal, November 2001

Speed improvement of multivariate algorithms by the method of postponed basis matrix multiplication journal, June 1994

Relating two proposed methods for speedup of algorithms for fitting two- and three-way principal component and related multilinear models journal, February 1997

Fast principal component analysis of large data sets based on information extraction journal, January 2002

Impact of Hierarchical Memory Systems On Linear Algebra Algorithm Design journal, March 1988

Improving the speed of multiway algorithms
journal, August 1998

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool
journal, January 2003

The kernel PCA algorithms for wide data. Part I: Theory and algorithms
journal, April 1997

Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution
journal, June 1998

Fast principal component analysis of large data sets
journal, November 2001

Speed improvement of multivariate algorithms by the method of postponed basis matrix multiplication
journal, June 1994

Relating two proposed methods for speedup of algorithms for fitting two- and three-way principal component and related multilinear models
journal, February 1997

Fast principal component analysis of large data sets based on information extraction
journal, January 2002

Impact of Hierarchical Memory Systems On Linear Algebra Algorithm Design
journal, March 1988