A provable SVD-based algorithm for learning topics in dominant admixture corpus

@inproceedings{Bansal2014APS,
  title={A provable SVD-based algorithm for learning topics in dominant admixture corpus},
  author={Trapit Bansal and Chiranjib Bhattacharyya and Ravi Kannan},
  booktitle={NIPS},
  year={2014}
}
Topic models, such as Latent Dirichlet Allocation (LDA), posit that documents are drawn from admixtures of distributions over words, known as topics. The inference problem of recovering topics from such a collection of documents drawn from admixtures, is NP-hard. Making a strong assumption called separability, [4] gave the first provable algorithm for inference. For the widely used LDA model, [6] gave a provable algorithm using clever tensor-methods. But [4, 6] do not learn topic vectors with… CONTINUE READING
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