Data Visualization White Papers
Information Marginalization on Subgraphs
Overview Real-world data often involves objects that exhibit multiple relationships. This paper presents a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. The paper shows that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, it formulates new algorithms for transduction and ranking in relational data, and quantifies the performance of new schemes on real world data - achieving good results in many problems.
| Publisher | Springer Science+Business Media | File Format | |
|---|---|---|---|
| Date Published | August 2006 | ||
| Format | White Papers | ||
| Topics | |||
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