A Distance-preserving Matrix Sketch

TL;DR

This paper introduces two novel algorithms for selecting subsets of rows and columns in large matrices to preserve relative distances, enhancing the accuracy of visualizations of big datasets.

Contribution

The paper presents new distance-preserving matrix sketch algorithms that improve the fidelity of visualizations by better maintaining original data relationships.

Findings

Algorithms outperform traditional methods in preserving distances

Effective on both artificial and real datasets

Enhance accuracy of large dataset visualizations

Abstract

Visualizing very large matrices involves many formidable problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points in the higher-dimensional space after reducing rows and columns to fit in a lower dimensional space. This aspect is important because conclusions based on faulty visual reasoning can be harmful. Judging dissimilar points as similar or similar points as dissimilar on the basis of a visualization can lead to false conclusions. To ameliorate this bias and to make visualizations of very large datasets feasible, we introduce two new algorithms that respectively select a subset of rows and columns of a rectangular matrix. This selection is designed to preserve relative distances as closely as possible. We compare our matrix sketch to more traditional alternatives on a variety of artificial and real datasets.