Fast Krylov Methods for N-Body Learning
Abstract
This paper addresses the issue of numerical computation in machine learning domains based on similarity metrics, such as kernel methods, spectral techniques and Gaussian processes. It presents a general solution strategy based on Krylov subspace iteration and fast N-body learning methods. The experiments show significant gains in computation and storage on datasets arising in image segmentation, object detection and dimensionality reduction. The paper also presents theoretical bounds on the stability of these methods.
Cite
Text
Freitas et al. "Fast Krylov Methods for N-Body Learning." Neural Information Processing Systems, 2005.Markdown
[Freitas et al. "Fast Krylov Methods for N-Body Learning." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/freitas2005neurips-fast/)BibTeX
@inproceedings{freitas2005neurips-fast,
title = {{Fast Krylov Methods for N-Body Learning}},
author = {Freitas, Nando D. and Wang, Yang and Mahdaviani, Maryam and Lang, Dustin},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {251-258},
url = {https://mlanthology.org/neurips/2005/freitas2005neurips-fast/}
}