发明名称 |
PARTIALLY SUPERVISED MACHINE LEARNING OF DATA CLASSIFICATION BASED ON LOCAL-NEIGHBORHOOD LAPLACIAN EIGENMAPS |
摘要 |
<p>A local-neighborhood Laplacian Eigenmap (LNLE) algorithm is provided for methods and systems for semi-supervised learning on manifolds of data points in a high- dimensional space. In one embodiment, an LNLE based method includes building an adjacency graph over a dataset of labelled and unlabelled points. The adjacency graph is then used for finding a set of local neighbors with respect to an unlabelled data point to be classified. An eigen decomposition of the local subgraph provides a smooth function over the subgraph. The smooth function can be evaluated and based on the function evaluation the unclassified data point can be labelled. In one embodiment, a transductive inference (TI) algorithmic approach is provided. In another embodiment, a semi-supervised inductive inference (SSII) algorithmic approach is provided for classification of subsequent data points. A confidence determination can be provided based on a number of labeled data points within the local neighborhood. Experimental results comparing LNLE and simple LE approaches are presented.</p> |
申请公布号 |
WO2006113248(A2) |
申请公布日期 |
2006.10.26 |
申请号 |
WO2006US13566 |
申请日期 |
2006.04.11 |
申请人 |
HONDA MOTOR CO., LTD.;RIFKIN, RYAN;ANDREWS, STUART |
发明人 |
RIFKIN, RYAN;ANDREWS, STUART |
分类号 |
G06F15/18 |
主分类号 |
G06F15/18 |
代理机构 |
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代理人 |
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主权项 |
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地址 |
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