Title

Hybrid Manifold Embedding

Keywords

Dimensionality reduction; geodesic clustering (GC); hybrid manifold embedding (HyME); locally conjugate discriminant projection (LCDP); supervised manifold learning.

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.

Publication Date

12-1-2014

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

Volume

25

Issue

12

Number of Pages

2295-2302

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TNNLS.2014.2305760

Socpus ID

84913556325 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84913556325

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