Name
Abstract | ||
|---|---|---|
In the past several decades, classifier design has attracted much attention. Inspired by the locality preserving idea of manifold learning, here we give a local linear regression (LLR) classifier. The proposed classifier consists of three steps: first, search k nearest neighbors of a pointed sample from each special class, respectively; second, reconstruct the pointed sample using the k nearest neighbors from each special class, respectively; and third, classify the test sample according to the minimum reconstruction error. The experimental results on the ETH80 database, the CENPAMI handwritten number database and the FERET face image database demonstrate that LLR works well, leading to promising image classification performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00521-014-1681-2 | Neural Computing and Applications |
Keywords | Field | DocType |
Linear regression,Manifold learning,Locality,Image classification | Data mining,Locality,Local regression,Reconstruction error,Artificial intelligence,Contextual image classification,Classifier (linguistics),Nonlinear dimensionality reduction,Linear regression,k-nearest neighbors algorithm,Pattern recognition,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 7-8 | 0941-0643 |
Citations | PageRank | References |
4 | 0.41 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wankou Yang | 1 | 535 | 34.68 |
| Karl Ricanek | 2 | 165 | 18.65 |
| Fumin Shen | 3 | 1868 | 91.49 |