Name
Abstract | ||
|---|---|---|
•KCDVD can automatically yield virtual dictionary used to represent the samples.•KCDVD can effectively address the undersampling problem in face recognition.•KCDVD exploits the coordinate descent scheme to solve the representation models.•KCDVD is easy to implement and is much faster than other similar methods.•KCDVD outperforms many state-of-the-art classification methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2017.10.001 | Pattern Recognition |
Keywords | Field | DocType |
Kernel sparse representation for classification (KSRC),Virtual dictionary,Coordinate descend,Face recognition | Kernel (linear algebra),Training set,Facial recognition system,Feature vector,Pattern recognition,Computer science,Sparse approximation,Exploit,Artificial intelligence,Coordinate descent,Machine learning | Journal |
Volume | Issue | ISSN |
76 | C | 0031-3203 |
Citations | PageRank | References |
9 | 0.44 | 33 |
Authors | ||
5 | ||