Fast Algorithms for Robust PCA via Gradient Descent
This work addresses computational bottlenecks in Robust PCA for applications like data analysis and machine learning, offering significant speed-ups, though it is incremental as it builds on existing statistical understanding.
The paper tackles the problem of Robust PCA in fully and partially observed settings by developing a non-convex optimization approach that reduces computational complexity, achieving a reduction from O(r^2d^2 log(1/ε)) to O(rd^2 log(1/ε)) in the fully observed case and O(r^4d log d log(1/ε)) in the partially observed case.
We consider the problem of Robust PCA in the fully and partially observed settings. Without corruptions, this is the well-known matrix completion problem. From a statistical standpoint this problem has been recently well-studied, and conditions on when recovery is possible (how many observations do we need, how many corruptions can we tolerate) via polynomial-time algorithms is by now understood. This paper presents and analyzes a non-convex optimization approach that greatly reduces the computational complexity of the above problems, compared to the best available algorithms. In particular, in the fully observed case, with $r$ denoting rank and $d$ dimension, we reduce the complexity from $\mathcal{O}(r^2d^2\log(1/\varepsilon))$ to $\mathcal{O}(rd^2\log(1/\varepsilon))$ -- a big savings when the rank is big. For the partially observed case, we show the complexity of our algorithm is no more than $\mathcal{O}(r^4d \log d \log(1/\varepsilon))$. Not only is this the best-known run-time for a provable algorithm under partial observation, but in the setting where $r$ is small compared to $d$, it also allows for near-linear-in-$d$ run-time that can be exploited in the fully-observed case as well, by simply running our algorithm on a subset of the observations.