Large Scale Graph Learning from Smooth Signals
This enables large-scale graph learning for data scientists by making it computationally feasible, though it is incremental as it builds on existing approximate nearest neighbor techniques.
The paper tackles the high computational cost of graph learning, which scales as O(n^2), by proposing an algorithm that reduces it to O(n log(n)) while maintaining quality close to the exact model.
Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a higher computational cost. In particular, the current state-of-the-art model cost is $\mathcal{O}(n^2)$ for $n$ samples. In this paper, we show how to scale it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$, with quality that approaches the exact graph learning model. Our algorithm uses known approximate nearest neighbor techniques to reduce the number of variables, and automatically selects the correct parameters of the model, requiring a single intuitive input: the desired edge density.