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M for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel, L1-norm based approach introduced by Kleinberg and Sandler 19 . We show that our algorithm performs extremely well on large datasets, with peak accuracy approaching that of supervised learning based on Support Vector Machines with large training sets. The method is based on the same idea that underlies Latent Semantic Indexing (LSI). We find.

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