Supervised Imagery Classification Based on Hierarchical Macro Manifold

Abstract:

Manifold learning has made many successful applications in the fields of dimensionality reduction, pattern recognition, and data visualization. In this paper we proposed hierarchical macro manifold (HMM) for the purpose of supervised classification. We construct hierarchical macro manifold based on the given training sets. The generalized regression neural network is employed to solve the out-of-sample problem. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4843-4846
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4843
• Online since: May 2014
• Authors: Hong Bing Huang*
• Keywords: Dimensionality Reduction, Generalized Regression Neural Network, Imagery Classification, Manifold Learning, Submanifold

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