Data Dependent Kernels in Nearly-Linear Time
Abstract
We propose a method to efficiently construct data dependent kernels which can make use of large quantities of (unlabeled) data. Our construction makes an approximation in the standard construction of semi-supervised kernels in Sindhwani et al. (2005). In typical cases these kernels can be computed in nearly-linear time (in the amount of data), improving on the cubic time of the standard construction, enabling large scale semi-supervised learning in a variety of contexts. The methods are validated on semi-supervised and unsupervised problems on data sets containing upto 64,000 sample points.
Cite
Text
Lever et al. "Data Dependent Kernels in Nearly-Linear Time." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Lever et al. "Data Dependent Kernels in Nearly-Linear Time." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/lever2012aistats-data/)BibTeX
@inproceedings{lever2012aistats-data,
title = {{Data Dependent Kernels in Nearly-Linear Time}},
author = {Lever, Guy and Diethe, Tom and Shawe-Taylor, John},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {685-693},
volume = {22},
url = {https://mlanthology.org/aistats/2012/lever2012aistats-data/}
}