Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
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.
Cite
Text
Bhatia et al. "Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation." Neural Information Processing Systems, 2018.Markdown
[Bhatia et al. "Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/bhatia2018neurips-genoja/)BibTeX
@inproceedings{bhatia2018neurips-genoja,
title = {{Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation}},
author = {Bhatia, Kush and Pacchiano, Aldo and Flammarion, Nicolas and Bartlett, Peter L and Jordan, Michael I},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {7016-7025},
url = {https://mlanthology.org/neurips/2018/bhatia2018neurips-genoja/}
}