In-Database Regression in Input Sparsity Time

TL;DR

This paper introduces fast, input-sparsity algorithms for computing subspace embeddings directly from database join results, enabling efficient high-accuracy regression without explicitly constructing large matrices.

Contribution

It presents novel input-sparsity algorithms for subspace embeddings tailored for join outputs, significantly reducing computational time for regression tasks.

Findings

Algorithms run in time proportional to non-zero entries in input tables

Achieves high-accuracy regression faster than prior FAQ-based methods

Empirical results show substantial speedups on real datasets

Abstract

Sketching is a powerful dimensionality reduction technique for accelerating algorithms for data analysis. A crucial step in sketching methods is to compute a subspace embedding (SE) for a large matrix $\mathbf{A} \in \mathbb{R}^{N \times d}$. SE's are the primary tool for obtaining extremely efficient solutions for many linear-algebraic tasks, such as least squares regression and low rank approximation. Computing an SE often requires an explicit representation of $\mathbf{A}$ and running time proportional to the size of $\mathbf{A}$. However, if $\mathbf{A}= \mathbf{T}_1 \Join \mathbf{T}_2 \Join \dots \Join \mathbf{T}_m$ is the result of a database join query on several smaller tables $\mathbf{T}_i \in \mathbb{R}^{n_i \times d_i}$, then this running time can be prohibitive, as $\mathbf{A}$ itself can have as many as $O(n_1 n_2 \cdots n_m)$ rows. In this work, we design subspace embeddings for database joins which can be computed significantly faster than computing the join. For the case of a two table join $\mathbf{A} = \mathbf{T}_1 \Join \mathbf{T}_2$ we give input-sparsity algorithms for computing subspace embeddings, with running time bounded by the number of non-zero entries in $\mathbf{T}_1,\mathbf{T}_2$. This results in input-sparsity time algorithms for high accuracy regression, significantly improving upon the running time of prior FAQ-based methods for regression. We extend our results to arbitrary joins for the ridge regression problem, also considerably improving the running time of prior methods. Empirically, we apply our method to real datasets and show that it is significantly faster than existing algorithms.