Efficient Frequent Directions Algorithm for Sparse Matrices

TL;DR

This paper introduces Sparse Frequent Directions, a streaming algorithm for sparse matrices that maintains strong accuracy guarantees while significantly reducing computational complexity, making it practical for large-scale data.

Contribution

It presents a novel variant of Frequent Directions optimized for sparse matrices, with improved runtime bounds and practical efficiency demonstrated empirically.

Findings

Achieves asymptotic improvements in runtime for sparse matrices

Maintains strong space-accuracy tradeoff guarantees

Demonstrates practical efficiency on real and synthetic data

Abstract

This paper describes Sparse Frequent Directions, a variant of Frequent Directions for sketching sparse matrices. It resembles the original algorithm in many ways: both receive the rows of an input matrix $A^{n \times d}$ one by one in the streaming setting and compute a small sketch $B \in R^{\ell \times d}$. Both share the same strong (provably optimal) asymptotic guarantees with respect to the space-accuracy tradeoff in the streaming setting. However, unlike Frequent Directions which runs in $O(nd\ell)$ time regardless of the sparsity of the input matrix $A$, Sparse Frequent Directions runs in $\tilde{O} (nnz(A)\ell + n\ell^2)$ time. Our analysis loosens the dependence on computing the Singular Value Decomposition (SVD) as a black box within the Frequent Directions algorithm. Our bounds require recent results on the properties of fast approximate SVD computations. Finally, we empirically demonstrate that these asymptotic improvements are practical and significant on real and synthetic data.