Name
Abstract | ||
|---|---|---|
In research of pattern recognition, we always want to achieve the correct classification rate according to the characteristics required. Feature extraction greatly affects the design and performance of the classifier, and it is one of the core issue of PR research. As an important component of pattern recognition, feature extraction has been paid close attention by many scholars, and currently has become one of the research hot spots in the field of pattern recognition. This article gives a general discussion of feature extraction, includes linear feature extraction and nonlinear feature extraction, and introduces the frontier methods of this field, at last discusses the development tendency of feature extraction. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10462-011-9225-y | Artif. Intell. Rev. |
Keywords | Field | DocType |
Pattern recognition,Feature extraction,Neural networks,Independent component analysis,Manifold learning | Data mining,Computer science,Feature (machine learning),Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Artificial neural network,Nonlinear dimensionality reduction,Classifier (linguistics),Pattern recognition,Feature (computer vision),Feature extraction,Independent component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 3 | 0269-2821 |
Citations | PageRank | References |
16 | 0.70 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shifei Ding | 1 | 1074 | 94.63 |
| Hong Zhu | 2 | 81 | 7.20 |
| Weikuan Jia | 3 | 123 | 20.12 |
| Chunyang Su | 4 | 154 | 9.62 |