Year: 2015
Type: preprint
Abstract: Matrices have become essential data representations for many large-scale problems in data analytics, and hence matrix sketching is a critical task. Although much research has focused on improving the error/size tradeoff under various sketching paradigms, the many forms of error bounds make these approaches hard to compare in theory and in practice. This paper attempts to categorize and compare most known methods under row-wise streaming updates with provable guarantees, and then to tweak some of these methods to gain practical improvements while retaining guarantees. For instance, we observe that a simple heuristic iSVD, with no guarantees, tends to outperform all known approaches in terms of size/error trade-off. We modify the best performing method with guarantees FrequentDirections under the size/error trade-off to match the performance of iSVD and retain its guarantees. We also demonstrate some adversarial datasets where iSVD performs quite poorly. In comparing techniques in the time/error trade-off, techniques based on hashing or sampling tend to perform better. In this setting we modify the most studied sampling regime to retain error guarantee but obtain dramatic improvements in the time/error trade-off. Finally, we provide easy replication of our studies on APT, a new testbed which makes available not only code and datasets, but also a computing platform with fixed environmental settings.
Source: arXiv (Cornell University)
Authors: Desai, Amey, Ghashami, Mina, Phillips, Jeff M.
Institution: University of Utah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: CIF21 DIBBs: STORM: Spatio-Temporal Online Reasoning and Management of Large Data, CAREER: Foundations for Geometric Analysis of Noisy Data, AF: Small: Synopsis Data Structures for Data Analysis in Shape Spaces, BIGDATA: Small: DCM: DA: Building a Mergeable and Interactive Distributed Data Layer for Big Data Summarization Systems
OpenAlex ID: W2952065427