ESammon: A Computationaly Enhanced Sammon Mapping based on Data Density.
Sammon mapping is a widely used visualization technique to display complex data from high- to low-dimensional space. However, its extensive computational cost may pose potential computational challenges to big data visualization. This paper proposes a computationally-enhanced Sammon mapping (ESammon) by leveraging the characteristics of spatial data density. Unlike the conventional Sammon, ESammon preserves critical pairwise distances between data points in the process of projection, instead of all distances. Specifically, we integrated the Directed-Acyclic-Graph (DAG) based data density characterization method to select the critical distances. The numerical results demonstrated that our ESammon can achieve comparable projection results as the conventional Sammon mapping while reducing the computational cost fromO(N2)toO(N).
Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, USA; Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA, USA.
Chanpaul Jin
CJ
Wang
Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, USA.
Hua
H
Fang
Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA, USA.
Honggang
H
Wang
Multidimensional scaling (MDS); Sammon mapping; data density
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