Partially supervised machine learning of data classification based on local-neighborhood Laplacian Eigenmaps
US7412425B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 14, 2005 |
| Grant date | Aug 12, 2008 |
| Priority date | — |
| Expiry date | Sep 21, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/21375
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
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