Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels
Abstract
We present how to perform exact large-scale multi-class Gaussian process classification with parameterized histogram intersection kernels. In contrast to previous approaches, we use a full Bayesian model without any sparse approximation techniques, which allows for learning in sub-quadratic and classification in constant time. To handle the additional model flexibility induced by parameterized kernels, our approach is able to optimize the parameters with large-scale training data. A key ingredient of this optimization is a new efficient upper bound of the negative Gaussian process log-likelihood. Experiments with image categorization tasks exhibit high performance gains with flexible kernels as well as learning within a few minutes and classification in microseconds for databases, where exact Gaussian process inference was not possible before.
Cite
Text
Rodner et al. "Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33765-9_7Markdown
[Rodner et al. "Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/rodner2012eccv-large/) doi:10.1007/978-3-642-33765-9_7BibTeX
@inproceedings{rodner2012eccv-large,
title = {{Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels}},
author = {Rodner, Erik and Freytag, Alexander and Bodesheim, Paul and Denzler, Joachim},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {85-98},
doi = {10.1007/978-3-642-33765-9_7},
url = {https://mlanthology.org/eccv/2012/rodner2012eccv-large/}
}