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Abstract: Given a collection of images (matrices) representing a class of objects we present a method for extracting the commonalities of the image space directly from the matrix representations (rather than from the vectorized representation which one would normally do in a PCA approach, for example). The general idea is to consider the collection of matrices as a tensor and to look for an approximation of its tensor-rank. The tensor-rank approximation is designed such that the SVD decomposition emerges ... (Update)
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BibTeX entry: (Update)
A. Shashua and A. Levin. Linear image coding for regression and classification using the tensor-rank principle. In Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, page in press, Hawai, 2001. http://citeseer.ist.psu.edu/shashua01linear.html More
@inproceedings{ shashua01linear,
author = "A. Shashua and A. Levin",
title = "Linear image coding for regression and classification using the tensor-rank
principle",
booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition, page in press, Hawai",
year = "2001",
url = "citeseer.ist.psu.edu/shashua01linear.html" }
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