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 language | English (US) |
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Title of host publication | Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005 |
Pages | 32-43 |
Number of pages | 12 |
State | Published - 2005 |
Externally published | Yes |
Event | 5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States Duration: Apr 21 2005 → Apr 23 2005 |
Other
Other | 5th SIAM International Conference on Data Mining, SDM 2005 |
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Country/Territory | United States |
City | Newport Beach, CA |
Period | 4/21/05 → 4/23/05 |
ASJC Scopus subject areas
- Software