Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
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- 作者：Erik Rodner ; Alexander Freytag ; Paul Bodesheim…
- 关键词：Large ; scale learning ; Gaussian processes ; Hyperparameter optimization ; Visual recognition
- 刊名：International Journal of Computer Vision
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：121
- 期：2
- 页码：253-280
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition;
- 出版者：Springer US
- ISSN：1573-1405
- 卷排序：121
文摘
We present new methods for fast Gaussian process (GP) inference in large-scale scenarios including exact multi-class classification with label regression, hyperparameter optimization, and uncertainty prediction. In contrast to previous approaches, we use a full Gaussian process model without sparse approximation techniques. Our methods are based on exploiting generalized histogram intersection kernels and their fast kernel multiplications. We empirically validate the suitability of our techniques in a wide range of scenarios with tens of thousands of examples. Whereas plain GP models are intractable due to both memory consumption and computation time in these settings, our results show that exact inference can indeed be done efficiently. In consequence, we enable every important piece of the Gaussian process framework—learning, inference, hyperparameter optimization, variance estimation, and online learning—to be used in realistic scenarios with more than a handful of data.