Rank priors for continuous non-linear dimensionality reduction
Name
Geiger-2009-Rank priors for continuous non-linear dimensionality reduction.pdf
Size
2.29 MB
Format
Adobe PDF
Checksum (MD5)
15fcacfe13bb40ff5ff10c121bd70d5a
Author(s)
Darrell, Trevor J.
Urtasun, Raquel
Geiger, Andreas
Date Issued
August 2009
Journal
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Geiger, A., R. Urtasun, and T. Darrell. “Rank priors for continuous non-linear dimensionality reduction.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 880-887. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
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 penalizes high dimensional spaces, and simultaneously optimize both the latent space and its intrinsic dimensionality in a continuous fashion. Ad-hoc initialization schemes are unnecessary with our approach; we initialize the latent space to the observation space and automatically infer the latent dimensionality. We report results applying our prior to various probabilistic non-linear dimensionality reduction tasks, and show that our method can outperform graph-based dimensionality reduction techniques as well as previously suggested initialization strategies. We demonstrate the effectiveness of our approach when tracking and classifying human motion.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPRW.2009.5206672
