Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35915
Title: Hybrid manifold embedding
Authors: Liu, Y
Liu, Y 
Chan, KCC 
Hua, KA
Keywords: Dimensionality reduction
Geodesic clustering (GC)
Hybrid manifold embedding (HyME)
Locally conjugate discriminant projection (LCDP)
Supervised manifold learning
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2014, v. 25, no. 12, p. 2295-2302 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method.
URI: http://hdl.handle.net/10397/35915
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2014.2305760
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