2-dimensional singular value decomposition for 2D maps and images

Chris Ding, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

77 Scopus citations

Abstract

For a set of 1D vectors, standard singular value decomposition (SVD) is frequently applied. For a set of 2D objects such as images or weather maps, we form 2dSVD, which computes principal eigenvectors of row-row and column-column covariance matrices, exactly as in the standard SVD. We study optimality properties of 2dSVD as low-rank approximation and show that it provides a framework unifying two recent approaches. Experiments on images and weather maps illustrate the usefulness of 2dSVD.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005
Pages32-43
Number of pages12
StatePublished - 2005
Externally publishedYes
Event5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States
Duration: Apr 21 2005Apr 23 2005

Other

Other5th SIAM International Conference on Data Mining, SDM 2005
CountryUnited States
CityNewport Beach, CA
Period4/21/054/23/05

ASJC Scopus subject areas

  • Software

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