A Unified Algorithmic Framework for Multi-Dimensional Scaling

Details A Unified Algorithmic Framework for Multi-Dimensional Scaling A Unified Algorithmic Framework for Multi-Dimensional Scaling

2010-03-02

arxiv.org/abs/1003.0529v2

Computer Science

Learning

18 pages, 7 figures. This version fixes a bug in the proof of Theorem 6.1 (dimensionality reduction for spherical data). The s

Scientific paper

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 \emph{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. 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 compliment to this result, we also extend the Johnson-Lindenstrauss Lemma to this spherical setting, where projecting to a random $O((1/\eps^2) \log n)$-dimensional sphere causes $\eps$-distortion.

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