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ESammon: A Computationaly Enhanced Sammon Mapping based on Data Density.

Chanpaul Jin Wang¹, Hua Fang², Honggang Wang³

¹Department 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.

International Conference on Computing, Networking, and Communications : [Proceedings]. International Conference on Computing, Networking and Communications

|September 27, 2016

Summary

This study introduces an enhanced Sammon mapping (ESammon) for big data visualization. ESammon significantly reduces computational cost from O(N^2) to O(N) while maintaining comparable projection quality.

Keywords:

Multidimensional scaling (MDS)Sammon mapping data density

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Area of Science:

Data Visualization
Computational Science
Machine Learning

Background:

Sammon mapping is a standard technique for reducing data dimensionality.
High computational cost of conventional Sammon mapping limits its application to big data.
Efficient visualization of large datasets remains a significant challenge.

Purpose of the Study:

To develop a computationally efficient Sammon mapping method for big data visualization.
To reduce the computational complexity of Sammon mapping without sacrificing projection accuracy.
To leverage spatial data density for optimizing the Sammon mapping process.

Main Methods:

Proposed computationally-enhanced Sammon mapping (ESammon).
Integrated Directed-Acyclic-Graph (DAG) based data density characterization to identify critical pairwise distances.
Modified Sammon mapping to preserve only critical distances instead of all distances.

Main Results:

ESammon achieves comparable projection results to conventional Sammon mapping.
Computational cost is reduced from O(N^2) to O(N).
Demonstrated effectiveness in big data visualization scenarios.

Conclusions:

ESammon offers a computationally efficient alternative to traditional Sammon mapping.
The method is suitable for visualizing large and complex datasets.
Preserving critical distances is key to achieving computational gains.