# Gen-Oja: A Two-time-scale approach for Streaming CCA

arXiv: https://arxiv.org/abs/1811.08393 · HTML: https://mlsanity.com/paper/1811.08393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems](https://mlsanity.com/paper/2112.14738/) (2021)

## Abstract (arXiv)

In this paper, we study the problems of principal Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm, Gen-Oja, 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.