A new quality assessment criterion for nonlinear dimensionality reduction

Authors:
Deyu Meng;Yee Leung;Zongben Xu
Affiliations:
Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an 710049, PR China;Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, PR China;Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an 710049, PR China
Venue:
Neurocomputing
Year:
2011

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Abstract

A new quality assessment criterion for evaluating the performance of the nonlinear dimensionality reduction (NLDR) methods is proposed in this paper. Differing from the current quality assessment criteria focusing on the local-neighborhood-preserving performance of the NLDR methods, the proposed criterion capitalizes on a new aspect, the global-structure-holding performance, of the NLDR methods. By taking both properties into consideration, the intrinsic capability of the NLDR methods can be more faithfully reflected, and hence more rational measurement for the proper selection of NLDR methods in real-life applications can be offered. The theoretical argument is supported by experiment results implemented on a series of benchmark data sets.