Loading patent…
AssigneeHonda Motor Co., Ltd.
InventorsRyan Rifkin, Stuart Andrews
Estimating the term…
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.
A computer based method for learning a label of an unlabelled data point from a plurality of data points, the method comprising:
A computer based system for learning a label of an unlabelled data point from a plurality of data points, the system comprising:
A computer based system for learning a label of an unlabelled data point from a plurality of data points, the system comprising:
A computer readable medium for learning a label of an unlabelled data point from a plurality of data points, the computer readable medium comprising software instructions that when executed in a computer processor cause a computer system to implement the steps of:
Dependencies are inferred from the claim text.