Building k-connected neighborhood graphs for isometric data embedding

IEEE Transactions on Pattern Analysis and Machine Intelligence
Li Yang

Abstract

Isometric data embedding using geodesic distance requires the construction of a connected neighborhood graph so that the geodesic distance between every pair of data points can be estimated. This paper proposes an approach for constructing k-connected neighborhood graphs. The approach works by applying a greedy algorithm to add each edge, in a nondecreasing order of edge length, to a neighborhood graph if end vertices of the edge are not yet k-connected on the graph. The k-connectedness between vertices is tested using a network flow technique by assigning every vertex a unit flow capacity. This approach is applicable to a wide range of data. Experiments show that it gives better estimation of geodesic distances than other approaches, especially when the data are undersampled or nonuniformly distributed.

References

Dec 23, 2000·Science·J B TenenbaumJ C Langford
Jan 5, 2002·Science·Mukund Balasubramanian, Eric L Schwartz
Mar 4, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Li Yang
Oct 22, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Li Yang
Jan 1, 1997·IEEE Transactions on Neural Networks·P Demartines, J Herault

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Citations

Apr 12, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Mingyu FanHujun Bao
Nov 26, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Dongfang Zhao, Li Yang
Jan 16, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Li Yang
Jul 18, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Deyu MengZongben Xu

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