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Rank priors for continuous non-linear dimensionality reduction (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Geiger, Andreas Urtasun, Raquel Darrell, Trevor |
| Description | In CVPR Discovering the underlying low-dimensional latent structure in high-dimensional perceptual observations (e.g., images, video) can, in many cases, greately 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. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Intrinsic Dimensionality Many Case Low-dimensional Latent Structure Global Optimum Human Motion Continuous Fashion Non-linear Dimensionality Reduction Method Continuous Non-linear Dimensionality Reduction Ad-hoc Initialization Scheme Initialization Strategy Graph-based Dimensionality Reduction Technique Latent Dimensionality Observation Space Latent Space High-dimensional Perceptual Observation Local Minimum High Dimensional Space Rank Prior |
| Content Type | Text |
| Resource Type | Article |
