ICML 2017poster57 citations
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition
Abstract
We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and on k. Furthermore, our algorithms are
BibTeX
@InProceedings{pmlr-v70-allen-zhu17b,
title = {Doubly Accelerated Methods for Faster {CCA} and Generalized Eigendecomposition},
author = {Zeyuan Allen-Zhu and Yuanzhi Li},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {98--106},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/allen-zhu17b/allen-zhu17b.pdf},
url = {https://proceedings.mlr.press/v70/allen-zhu17b.html},
abstract = {We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and on k. Furthermore, our algorithms are