Semi-supervised learning on graphs : a statistical approach
Abstract/Contents
- Abstract
- Data on graphs are growing tremendously in size and prevalence these days; consider the World Wide Web graph or the Facebook social network. In semi-supervised learning on graphs, features observed at one node are used to estimate missing values at other nodes. Many prediction methods have been proposed in the machine learning community over the past few years. In this thesis we show that several such proposals are equivalent to kriging predictors based on a fixed covariance matrix driven by the link structure of the graph. We then propose a data-driven estimator of the correlation structure that exploits patterns among the observed response values. We also show how we can scale some of the algorithms to large graphs. Finally, we investigate the fundamental smoothness assumption underlying many prediction methods by exploring some normality properties arising from empirical data analysis.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Xu, Ya, (Researcher) |
|---|---|
| Associated with | Stanford University, Department of Statistics |
| Primary advisor | Owen, Art B |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Tibshirani, Robert |
| Thesis advisor | Wong, Wing Tak Jack Wong |
| Advisor | Tibshirani, Robert |
| Advisor | Wong, Wing Tak Jack Wong |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Ya Xu. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2010. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2010 by Ya Xu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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