NeurIPS 2018poster22 citations
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L Bartlett, Michael I Jordan
Abstract
In this paper, we study the problems of principle Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm for these problems. We prove the global convergence of our algorithm, borrowing ideas from the theory of fast-mixing Markov chains and two-Time-Scale Stochastic Approximation, showing that it achieves the optimal rate of convergence. In the process, we develop tools for understanding stochastic processes with Markovian noise which might be of independent interest.
BibTeX
@inproceedings{NEURIPS2018_1b318124,
author = {Bhatia, Kush and Pacchiano, Aldo and Flammarion, Nicolas and Bartlett, Peter L and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Gen-Oja: Simple \& Efficient Algorithm for Streaming Generalized Eigenvector Computation},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/1b318124e37af6d74a03501474f44ea1-Paper.pdf},
volume = {31},
year = {2018}
}