Description: Infrared moving target detection and tracking based on tensor locality
preserving projection
Hong Li a, Yantao Wei a, Luoqing Li b,*, Yuan Y. Tang c,d Platform: |
Size: 229376 |
Author:massin34 |
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Description: 有监督直接局部保持投影的人脸识别Directly supervised locality preserving projection of the face recognition-Directly supervised locality preserving projection of the face recognition Platform: |
Size: 1073152 |
Author:nety |
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Description: 该方法是基于保持数据局部流形的降维方法，在降维后的低维空间能较好的保持其局部子流形-Locality Preserving Projection (You need to download LGE.m as well as constructW.m). Platform: |
Size: 9216 |
Author:caolinlin |
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Description: 提出的正交LPP具有的优良特性是：能够较好的重构数据，并包含较好的判别能力-The orthogonal locality
preserving projection (OLPP) method produces orthogonal basis functions
and can have more locality preserving power than LPP. Since the locality
preserving power is potentially related to the discriminating power, the
OLPP is expected to have more discriminating power than LPP Platform: |
Size: 1581056 |
Author:刘建飞 |
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Description: 人脸识别的LPP方法的源代码，保局投影(LPP)作为拉普拉斯特征映射的一种线性逼近可以较好的反映样本的流形结构-LPP method for face recognition source code, locality preserving projection (LPP) manifold structure as a linear Laplasse feature mapping approach can better reflect the sample Platform: |
Size: 29696 |
Author:黄文聪 |
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Description: Locality Preserving Projections(LPP),局部保留投影，可以找出高维空间中的数据在低维空间中的投影，并保留了数据之间的相关性-Locality Preserving Projections (LPP), partial retention projection, you can find the data in high-dimensional space in a low dimensional space projection, and retains the correlation between data Platform: |
Size: 2048 |
Author:去伦敦 |
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Description: 该源代码是人脸识别中的二维单项局部保持投影算法2DDLPP，源代码下载后就可以执行，简单，易理解。-The source code is a two-dimensional face recognition single locality preserving projection algorithm 2DDLPP, after downloading the source code can be executed, simple and easy to understand. Platform: |
Size: 188416 |
Author:李泷 |
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Description: 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 Platform: |
Size: 1024 |
Author:骕骦 |
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Search - Locality Preserving Projection