Rank priors for continuous non-linear dimensionality reduction

  title={Rank priors for continuous non-linear dimensionality reduction},
  author={Andreas Geiger and Raquel Urtasun and Trevor Darrell},
  journal={2009 IEEE Conference on Computer Vision and Pattern Recognition},
Discovering the underlying low-dimensional latent structure in high-dimensional perceptual observations (e.g., images, video) can, in many cases, greatly improve performance in recognition and tracking. However, non-linear dimensionality reduction methods are often susceptible to local minima and perform poorly when initialized far from the global optimum, even when the intrinsic dimensionality is known a priori. In this work we introduce a prior over the dimensionality of the latent space that… CONTINUE READING
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