PARTIALLY SUPERVISED MACHINE LEARNING OF DATA CLASSIFICATION BASED ON LOCAL-NEIGHBORHOOD LAPLACIAN EIGENMAPS
专利汇可以提供PARTIALLY SUPERVISED MACHINE LEARNING OF DATA CLASSIFICATION BASED ON LOCAL-NEIGHBORHOOD LAPLACIAN EIGENMAPS专利检索，专利查询，专利分析的服务。并且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.，下面是PARTIALLY SUPERVISED MACHINE LEARNING OF DATA CLASSIFICATION BASED ON LOCAL-NEIGHBORHOOD LAPLACIAN EIGENMAPS专利的具体信息内容。
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