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...
Main Authors: | Darrell, Trevor J., Urtasun, Raquel, Geiger, Andreas |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers
2010
|
Online Access: | http://hdl.handle.net/1721.1/59287 |
Similar Items
-
Rank Priors for Continuous Non-Linear Dimensionality Reduction
by: Stiefelhagen, Rainer, et al.
Published: (2008) -
Discriminative Gaussian Process Latent Variable Model for Classification
by: Urtasun, Raquel, et al.
Published: (2007) -
Transfering Nonlinear Representations using Gaussian Processes with a Shared Latent Space
by: Urtasun, Raquel, et al.
Published: (2007) -
Unsupervised Distributed Feature Selection for Multi-view Object Recognition
by: Christoudias, C. Mario, et al.
Published: (2008) -
Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space
by: Urtasun, Raquel, et al.
Published: (2008)