Rank priors for continuous non-linear dimensionality reduction
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 pe...
| Päätekijät: | , , |
|---|---|
| Muut tekijät: | |
| Aineistotyyppi: | Artikkeli |
| Kieli: | en_US |
| Julkaistu: |
Institute of Electrical and Electronics Engineers
2010
|
| Linkit: | http://hdl.handle.net/1721.1/59287 |