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Geometric Models with Co-occurrence Groups

A geometric model of sparse signal representations is introduced for classes of signals. It is computed by optimizing co-occurrence groups with a maximum likelihood estimate calculated with a Bernoulli mixture model. Applications to face image compression and MNIST digit classification illustrate the applicability of this model.

preprint2011arXivOpen access
Joan BrunaStéphane Mallat
math.ITInformation TheoryComputer Vision
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Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Joan BrunaStéphane Mallat

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