Online Sparse Matrix Gaussian Process Regression and Vision Applications

13 years 3 months ago
Online Sparse Matrix Gaussian Process Regression and Vision Applications
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP is based on the observation that for kernels with local support, the Gram matrix is typically sparse. Maintaining and updating the sparse Cholesky factor of the Gram matrix can be done efficiently using Givens rotations. This leads to an exact, online algorithm whose update time scales linearly with the size of the Gram matrix. Further, if approximate updates are permissible, the Cholesky factor can be maintained at a constant size using hyperbolic rotations to remove certain rows and columns corresponding to discarded training examples. We demonstrate that, using these matrix downdates, online hyperparameter estimation can be included without affecting the linear runtime complexity of the algorithm. The OSMGP algorithm is applied to headpose estimation and visual tracking problems. Experimental results demon...
Ananth Ranganathan, Ming-Hsuan Yang
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2008
Where ECCV
Authors Ananth Ranganathan, Ming-Hsuan Yang
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