Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps

Quan Sheng Jiang; Su Ping Li

doi:10.4028/www.scientific.net/AMR.403-408.2679

Paper Titles

The Research of Virtual Design and Parts Library System Oriented to High Voltage Switchgear
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The Research on the Action Modes of Laser Shock Wave Affected by Different Confinement Overlays
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Study of Diffusion Characteristics of Pollutant in Flowing Water
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Determination of Melamine and its Analogues in Food
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Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps
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The Paging Mechanism and MMC Module in XS128 Flash
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Research on Component-Based Software Reuse Technology
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Neural Network Control of EPS Based on the Cerebellum Model
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On Upper Bound of the Quality of Service Performance in the Gateway of Wireless Mesh Networks
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Classification Criterion Based Neighborhood...

Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps

Abstract:

Manifold learning algorithms are nonlinear dimensionality reduction algorithms rising in recent years. Laplacian Eigenmaps is a typical manifold learning algorithms. Aim to the difficulty of selecting neighborhood parameter on the algorithm, a neighborhood parameter optimization method based on classification criterion is proposed in the paper. From the point of the classification performance, the classification criterion function is constructed to reflect the distance of within-class and between-class. The optimization of the neighborhood is obtained according to the minimum of the criterion function. The experimental results on IRIS validate the optimization of the neighborhood and the effectiveness of feature classification.