Big data visualization: Review of techniques and datasets

Pena, Luis Eder Velazquez; Rodríguez-Mazahua, Lisbeth; Alor-Hernández, Giner; Zepahua, Beatriz Alejandra Olivares; Camarena, Silvestre Gustavo Peláez; Machorro-Cano, Isaac

doi:10.1109/cimps.2017.8169944

Cited by 5 publications

(1 citation statement)

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“…We first discuss operations on rows (to reduce the number of points n) and then discuss operations on columns (to reduce dimensionality of points p). For general surveys of big data visualization methods, see (Unwin et al, 2007;Ali et al, 2016;Liu et al, 2017;Peña et al, 2017). For more detail on feature extraction and dimensionality reduction in visual analytics, see Guyon et al (2006); Fekete and Plaisant (2002); Krause et al (2016).…”

Section: Related Workmentioning

confidence: 99%

A Distance-preserving Matrix Sketch

Wilkinson,

Luo

2020

Preprint

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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 a new algorithm that selects 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.

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