文件名称:GoLPP
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D MANUSC
Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP
integrated the graph construction and a specific dimensionality reduction process (i.e.
LPP) into a unified framework, which results in a simultaneous learning for optimal
graph and projection matrix. From the experimental results in [20], it was demonstrated
that the GoLPP outperformed the classical LPP which is based on k nearest neighbor -
Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP
integrated the graph construction and a specific dimensionality reduction process (i.e.
LPP) into a unified framework, which results in a simultaneous learning for optimal
graph and projection matrix. From the experimental results in [20], it was demonstrated
that the GoLPP outperformed the classical LPP which is based on k nearest neighbor
Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP
integrated the graph construction and a specific dimensionality reduction process (i.e.
LPP) into a unified framework, which results in a simultaneous learning for optimal
graph and projection matrix. From the experimental results in [20], it was demonstrated
that the GoLPP outperformed the classical LPP which is based on k nearest neighbor -
Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP
integrated the graph construction and a specific dimensionality reduction process (i.e.
LPP) into a unified framework, which results in a simultaneous learning for optimal
graph and projection matrix. From the experimental results in [20], it was demonstrated
that the GoLPP outperformed the classical LPP which is based on k nearest neighbor
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GoLPP\GoLPP.m
GoLPP
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