An Improved Isometric Mapping Algorithm Based on New Geodesic Distance and Its Application in theFeature Extraction of Acoustic Targets

doi:10.3969/j.issn.1000-1093.2012.10.005

Abstract

Abstract: A drawback of the isometric mapping (ISOMAP) algorithm is topological instability, that is, the algorithm is sensitive to the noise, which makes ISOMAP do a bad performance when used in the feature extraction of acoustic targets in the battlefield. To solve the problem, this paper proposes a new method for more robust approximation of the geodesic distances of ISOMAP using a viewpoint of electric circuits. The charge times are used to construct new distance measures. In this way, we achieve both the stability of local methods and the global approximation property of global methods. Experimental results prove that the new distance measures can achieve significant advancement over the former method, which can improve the system performance effectively.

Key words: information processing, acoustic target recognition, manifold learning, isometric mapping arithmetic, Laplacian eigenmap arithmetic

CLC Number:

TB56

LIU Hui, YANG Jun-an, WANG Yi, CAI Xue-liang. An Improved Isometric Mapping Algorithm Based on New Geodesic Distance and Its Application in theFeature Extraction of Acoustic Targets[J]. Acta Armamentarii, 2012, 33(10): 1178-1184.

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