Name
Abstract | ||
|---|---|---|
We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new method to solve it from the viewpoint of distance metric learning. The method involves the learning of two mappings of the heterogeneous objects to a unified Euclidean space representing the network topology of the bipartite graph, where the graph is easy to infer. The algorithm can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of compound-protein interaction network reconstruction from chemical structure data and genomic sequence data. |
Year | Venue | Keywords |
|---|---|---|
2008 | NIPS | bipartite graph |
Field | DocType | Citations |
Adjacency matrix,Complete bipartite graph,Edge-transitive graph,Line graph,Computer science,Simplex graph,Assignment problem,Artificial intelligence,Lattice graph,Voltage graph,Machine learning | Conference | 18 |
PageRank | References | Authors |
1.42 | 11 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshihiro Yamanishi | 1 | 1268 | 83.44 |