Compressive PCA for Low-Rank Matrices on Graphs

TL;DR

This paper presents a fast, graph-based compressive PCA method for recovering low-rank matrices from sampled data, significantly reducing computational costs compared to traditional PCA methods.

Contribution

It introduces a novel framework leveraging graph eigenvectors and RIP conditions for efficient low-rank matrix recovery from sampled measurements.

Findings

Achieves p^2/k speed-up over Robust PCA.

Successfully recovers large matrices 100 times faster than RPCA.

Provides theoretical guarantees for sampling and decoding methods.

Abstract

We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between their rows and columns. We leverage this property to recover the non-linear low-rank structures efficiently from sampled data measurements, with a low cost (linear in n). First, a Resrtricted Isometry Property (RIP) condition is introduced for efficient uniform sampling of the rows and columns of such matrices based on the cumulative coherence of graph eigenvectors. Secondly, a state-of-the-art fast low-rank recovery method is suggested for the sampled data. Finally, several efficient, parallel and parameter-free decoders are presented along with their theoretical analysis for decoding the low-rank and cluster indicators for the full data matrix. Thus, we overcome the computational limitations of the standard linear low-rank recovery methods for big datasets. Our method can also be seen as a major step towards efficient recovery of non- linear low-rank structures. For a matrix of size n X p, on a single core machine, our method gains a speed up of $p^2/k$ over Robust Principal Component Analysis (RPCA), where k << p is the subspace dimension. Numerically, we can recover a low-rank matrix of size 10304 X 1000, 100 times faster than Robust PCA.