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Abstract: We introduce a new classification algorithm based on the concept of Symmetric Maximized Minimal distance in Subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, SMMS tries to identify a subspace in which all the authentic samples are close to each other and all the imposter samples are far away from the authentic samples. The optimality of the subspace is determined by maximizing the minimal distance between the authentic samples and the... (Update)
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BibTeX entry: (Update)
@misc{ zhang-classification,
author = "Wende Zhang and Tsuhan Chen",
title = "Classification Based on Symmetric Maximized Minimal Distance in Subspace
(SMMS)",
url = "citeseer.ist.psu.edu/582318.html" }
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