Title

Dimensionality Reduction In Hyperbolic Data Spaces: Bounding Reconstructed-Information Loss

Abstract

We have started to see non-Euclidean geometries used in many applications, including visualization, network measurement, and geometric routing. Therefore, we need new mechanisms for managing and exploring non-Euclidean data. In this paper, we specifically address the dimensionality reduction problem for Hyperbolic data that are represented by the Poincare Disk model. A desirable property of our solution is its reconstructed boundedness. In other words, we can reconstruct the data from its dimension-reduced version to obtain an approximation whose deviation from the original data is bounded and independent of the boundary measure of the original data space. We also propose a similarity search technique as an application of our reduction approach. We provide both theoretical and simulation analyses to substantiate the findings of our work. © 2008 IEEE.

Publication Date

9-15-2008

Publication Title

Proceedings - 7th IEEE/ACIS International Conference on Computer and Information Science, IEEE/ACIS ICIS 2008, In conjunction with 2nd IEEE/ACIS Int. Workshop on e-Activity, IEEE/ACIS IWEA 2008

Number of Pages

133-139

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIS.2008.82

Socpus ID

51349117815 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/51349117815

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