Name
Abstract | ||
|---|---|---|
In this paper, we propose a feature selection method that uses graph cuts based on both relevance and redundancy of features. The feature subset is derived by an optimization using a novel criterion which consists of two terms: relevance and redundancy. This kind of criterion has been proposed elsewhere, but previously proposed criteria are hard to optimize In contrast, our criterion is designed to satisfy submodularity so that we can obtain a globally optimal feature subset in polynomial time using graph cuts. Experimental results show that the proposed method works well, especially in the case of a medium-size subset where existing approaches are weak because of the many possible feature combinations. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | Machine learning, feature selction, graph cut, submodular function |
Field | DocType | ISSN |
Cut,Graph theory,Strength of a graph,Pattern recognition,Feature selection,Computer science,Feature (computer vision),Null graph,Redundancy (engineering),Artificial intelligence,Graph (abstract data type) | Conference | 1522-4880 |
Citations | PageRank | References |
2 | 0.36 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masato Ishii | 1 | 5 | 2.82 |
| Atsushi Sato | 2 | 23 | 5.54 |