Natural data structure extracted from neighborhood-similarity graphs. (February 2019)

• Record Type: Journal Article
• Authors: Lorimer, Tom; Kanders, Karlis; Stoop, Ruedi
• Is Part Of: Chaos, solitons and fractals. Volume 119(2019)
• Page Start: 326
• Page End: 331
• Publication Date: 2019-02
• Subjects: Data complexity -- Data networks -- Big data -- Clustering; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2018.12.033
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: 'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality reduction step that inherently distorts the data structure. For a similar analysis, clustering methods are also often used. These methods introduce a bias as well, either by starting from the assumption of a particular, often geometric, property of the clusters, or by using iterative schemes to enhance cluster contours, with consequences that are hard to control. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. Here, we propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel method.