A comment on "Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding"

Authors:
Caiming Zhong;Duoqian Miao
Affiliations:
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China and College of Science and Technology, Ningbo University, Ningbo 315211, PR China and Tongji Branch, ...;School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China and Tongji Branch, National Engineering and Technology Center of High Performance Computer, Shanghai ...
Venue:
Pattern Recognition
Year:
2009

Citing 1
Cited 1

Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding

Pattern Recognition

Adaptive Neighborhood Select Based on Local Linearity for Nonlinear Dimensionality Reduction

ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence

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Abstract

A geodesic distance-based approach to build the neighborhood graph for isometric embedding is proposed to deal with the highly twisted and folded manifold by Wen et al. [Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding, Pattern Recognition 41 (2008) 2226-2236]. This comment is to identify the error in their example and the ineffectiveness of their algorithm.