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Title: Geometrically local embedding in manifolds for dimension reduction
Authors: Ge, S.S. 
He, H.
Shen, C.
Keywords: Dimension reduction
Geometry distance
Linear manifolds
Issue Date: Apr-2012
Citation: Ge, S.S., He, H., Shen, C. (2012-04). Geometrically local embedding in manifolds for dimension reduction. Pattern Recognition 45 (4) : 1455-1470. ScholarBank@NUS Repository.
Abstract: In this paper, geometrically local embedding (GLE) is presented to discover the intrinsic structure of manifolds as a method in nonlinear dimension reduction. GLE is able to reveal the inner features of the input data in the lower dimension space while suppressing the influence of outliers in the local linear manifold. In addition to feature extraction and representation, GLE behaves as a clustering and classification method by projecting the feature data into low-dimensional separable regions. Through empirical evaluation, the performance of GLE is demonstrated by the visualization of synthetic data in lower dimension, and the comparison with other dimension reduction algorithms with the same data and configuration. Experiments on both pure and noisy data prove the effectiveness of GLE in dimension reduction, feature extraction, data visualization as well as clustering and classification. © 2011 Elsevier Ltd All rights reserved.
Source Title: Pattern Recognition
ISSN: 00313203
DOI: 10.1016/j.patcog.2011.09.022
Appears in Collections:Staff Publications

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