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Multimode Data Analysis for Decision Making

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Multiple Criteria Decision Making

Abstract

We consider an extension of eigenvector analysis to the data represented by many-way matrices. The results obtained from a non-linear eigenvalue problem can be interpreted as principal components (PC) of the items in each direction of a multimode matrix. Such generalized PC can be used for clarifying the internal structural relationships between variables, visualization of multispace data, ranking objects, and other aims of multicriteria decision analysis.

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Reference

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Author information

Authors and Affiliations

  1. Faculty of Management, Tel Aviv University, Ramat Aviv, 69978, Israel

    Stan Lipovetsky

Authors
  1. Stan Lipovetsky
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Editor information

Editors and Affiliations

  1. Energy Research Group and Institute of Traffic and Transportation, National Chiao Tung University, Taipai, Taiwan Republic of China

    G. H. Tzeng

  2. Department of Industrial Engineering, National Tsing Hua University, Hsinchu, Taiwan Republic of China

    H. F. Wang  & U. P. Wen  & 

  3. School of Business, University of Kansas, 66045-2003, Lawrence, Kansas, USA

    P. L. Yu

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© 1994 Springer-Verlag New York, Inc.

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Lipovetsky, S. (1994). Multimode Data Analysis for Decision Making. In: Tzeng, G.H., Wang, H.F., Wen, U.P., Yu, P.L. (eds) Multiple Criteria Decision Making. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2666-6_28

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  • DOI: https://doi.org/10.1007/978-1-4612-2666-6_28

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7626-5

  • Online ISBN: 978-1-4612-2666-6

  • eBook Packages: Springer Book Archive

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