Exploring high-dimensional data through locally enhanced projections. (October 2018)

• Record Type: Journal Article
• Authors: Lai, Chufan; Zhao, Ying; Yuan, Xiaoru
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 144
• Page End: 156
• Publication Date: 2018-10
• Subjects: Dimension-reduced projection -- Local data analysis -- High-dimensional data -- Subspace analysis; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.08.006
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Dimension reduced projections approximate the high-dimensional distribution by accommodating data in a low-dimensional space. They generate good overviews, but can hardly meet the needs of local relational/dimensional data analyses. On the one hand, layout distortions in linear projections largely harm the perception of local data relationships. On the other hand, non-linear projections seek to preserve local neighborhoods but at the expense of losing dimensional contexts. A sole projection is hardly enough for local analyses with different focuses and tasks. In this paper, we propose an interactive exploration scheme to help users customize a linear projection based on their point of interests (POIs) and analytic tasks. First, users specify their POI data interactively. Then regarding different tasks, various projections and subspaces are recommended to enhance certain features of the POI. Furthermore, users can save and compare multiple POIs and navigate their explorations with a POI map. Via case studies with real-world datasets, we demonstrate the effectiveness of our method to support high-dimensional local data analyses.