Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling

Georges Siolas 1 Florence d'Alché-Buc 1
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We present a generative model for constructing continuous word representations using mixtures of probabilistic PCAs. Applied to co-occurrence data, the model performs word clustering and allows the visualization of each cluster in a reduced space. In combination with a simple document model, it permits the definition of low-dimensional Fisher scores which are used as document features. We investigate the models’ potential through kernel-based methods using the corresponding Fisher kernels.
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Conference papers
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Submitted on : Tuesday, June 27, 2017 - 11:47:00 AM
Last modification on : Thursday, March 21, 2019 - 1:11:49 PM

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Georges Siolas, Florence d'Alché-Buc. Mixtures of Probabilistic PCAs and Fisher Kernels for Word and Document Modeling. International Conference on Artificial Neural Networks - ICANN 2002, Aug 2002, Madrid, Spain. pp.769-774, ⟨10.1007/3-540-46084-5_125⟩. ⟨hal-01548185⟩

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