Kernel Orthogonal Neighborhood Preserving Discriminant Analysis

Xing Zhu Liang; Jing Zhao Li; Yu E Lin

doi:10.4028/www.scientific.net/AMR.339.571

Paper Titles

The Simulation of Dropped Objects on the Offshore Structure
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Stress Analysis of Side Plates on the Logging Truck’s Cable Reel
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Design and Application of Forced Heat Dispersing Device of Superdeep Drilling Rig in High Temperature
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The Structure and Technical Parameters Analysis of Snubbing Unit
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Kernel Orthogonal Neighborhood Preserving Discriminant Analysis
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Process and Corrosion Resistance of Plasma-Sprayed NiCoCrAlY/(Al₂O_3-13%TiO₂) Coatings on AZ91D Magnesium Alloy
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Research and Application of Expansion Evaluation Device for Solid Expandable Tubular
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Study on Drifting Drilling Assembly and Casing String’s Stiffness Ratio
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Design Research of the Hydraulic Stopple Plugging Machine for Pipeline Maintenance under Pressure
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 339Kernel Orthogonal Neighborhood Preserving...

Kernel Orthogonal Neighborhood Preserving Discriminant Analysis

Abstract:

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kernel Orthogonal Neighborhood Preserving Discriminant Analysis (KONPDA). A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample. Then running KONPDA in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KONPDA method achieves higher recognition rate than the ONPDA method and other kernel-based learning algorithms.