2010 UniversalMultiDimensionalScalin

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Multi-dimensional scaling, dimensionality reduction.

Abstract

In this paper, we propose a unified algorithmic framework for solving many known variants of MDS. Our algorithm is a simple iterative scheme with guaranteed convergence, and is modular; by changing the internals of a single subroutine in the algorithm, we can switch cost functions and target spaces easily. In addition to the formal guarantees of convergence, our algorithms are accurate; in most cases, they converge to better quality solutions than existing methods in comparable time. Moreover, they have a small memory footprint and scale effectively for large data sets. We expect that this framework will be useful for a number of MDS variants that have not yet been studied.

 Our framework extends to embedding high-dimensional points lying on a sphere to points on a lower dimensional sphere, preserving geodesic distances. As a complement to this result, we also extend the Johnson-Lindenstrauss Lemma to this spherical setting, by showing that projecting to a random [math]\displaystyle{ O((1/\varepsilon^2) \log n) }[/math]-dimensional sphere causes only an [math]\displaystyle{ \varepsilon }[/math]-distortion in the geodesic distances.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 UniversalMultiDimensionalScalinArvind Agarwal
Jeff M. Phillips
Suresh Venkatasubramanian
Universal Multi-dimensional ScalingKDD-2010 Proceedingshttp://www.cs.utah.edu/~suresh/papers/smds/kdd10-final.pdf10.1145/1835804.18359482010