Spatial Random Sampling: A Structure-Preserving Data Sketching Tool

Keywords

Big data; clustering; column sampling; data sketching; random embedding; unit sphere

Abstract

Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters. Also, adaptive sampling can often provide accurate low rank approximations, yet may fall short of producing descriptive data sketches, especially when the cluster centers are linearly dependent. Motivated by that, this letter introduces a novel randomized column sampling tool dubbed spatial random sampling (SRS), in which data points are sampled based on their proximity to randomly sampled points on the unit sphere. The most compelling feature of SRS is that the corresponding probability of sampling from a given data cluster is proportional to the surface area the cluster occupies on the unit sphere, independently of the size of the cluster population. Although it is fully randomized, SRS is shown to provide descriptive and balanced data representations. The proposed idea addresses a pressing need in data science and holds potential to inspire many novel approaches for analysis of big data.

Publication Date

9-1-2017

Publication Title

IEEE Signal Processing Letters

Volume

24

Issue

9

Number of Pages

1398-1402

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/LSP.2017.2723472

Socpus ID

85023201871 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85023201871

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