Shared Kernel Information Embedding for Discriminative Inference

  title={Shared Kernel Information Embedding for Discriminative Inference},
  author={Roland Memisevic and Leonid Sigal and David J. Fleet},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
Latent variable models, such as the GPLVM and related methods, help mitigate overfitting when learning from small or moderately sized training sets. Nevertheless, existing methods suffer from several problems: 1) complexity, 2) the lack of explicit mappings to and from the latent space, 3) an inability to cope with multimodality, and 4) the lack of a well-defined density over the latent space. We propose an LVM called the Kernel Information Embedding (KIE) that defines a coherent joint density… CONTINUE READING
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Shared Kernel Information Embedding for Discriminative Inference

  • L. Sigal, R. Memisevic, D. Fleet
  • Proc. IEEE CS Conf. Computer Vision and Pattern…
  • 2009
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