Name
Abstract | ||
|---|---|---|
Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2551724 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Machine learning algorithms,Pattern recognition,Sun,Clustering algorithms,Computational modeling,Robustness,Algorithm design and analysis | Data mining,Dimensionality reduction,Feature selection,Computer science,Feature (machine learning),Artificial intelligence,Feature vector,Multi-task learning,Pattern recognition,Feature (computer vision),Feature extraction,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
28 | 7 | 2162-237X |
Citations | PageRank | References |
63 | 1.25 | 93 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Gui | 1 | 700 | 25.72 |
| Zhenan Sun | 2 | 2379 | 139.49 |
| Shuiwang Ji | 3 | 2579 | 122.25 |
| Dacheng Tao | 4 | 19032 | 747.78 |
| Tieniu Tan | 5 | 11681 | 744.35 |