Name
Abstract | ||
|---|---|---|
The task of feature selection is to find the optimal feature subset such that an appropriate criterion is optimized. It can be seen as a special subspace learning task, where the projection matrix is constrained to be selection matrix. In this paper, a novel unsupervised graph embedded feature selection (GEFS) method is derived from the perspective of incorporating the projected k-means with Stiefel manifold regularization. To achieve more statistical and structural properties, we directly embed unsupervised feature selection algorithm into a clustering algorithm via sparse learning to suppress the projected matrix to be row sparse. Comparative experiments demonstrate the effectiveness of our proposed algorithm in comparison with the traditional methods for feature selection. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Feature selection, K-means, Graph embedded |
Field | DocType | ISSN |
k-means clustering,Subspace topology,Pattern recognition,Feature selection,Computer science,Matrix (mathematics),Stiefel manifold,Feature extraction,Artificial intelligence,Cluster analysis,Sparse matrix | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohao Cai | 1 | 73 | 3.61 |
| Rui Zhang | 2 | 117 | 9.53 |
| Feiping Nie | 3 | 7061 | 309.42 |
| Xuelong Li | 4 | 15049 | 617.31 |