Name
Abstract | ||
|---|---|---|
Metric learning for KNN has attracted increasing attentions in the field of machine learning (e.g., based on the parametric form of Mahalanobis distance). A good distance metric is also the foundation for other machine learning models, for example, a Gaussian RBF kernel is constructed upon distance metric defined in the feature vector space. However, besides the KNN classifier, there is little research work on learning a good distancemetric for distance-basedmodels. In this paper, we propose a novel algorithmto learn aMahalanobis-distance type metric for Gaussian RBF kernels. We conduct experiments on 5 data sets from the UCI Machine Learning Repository database and two face recognition data sets. The classification results show that the proposed algorithm can outperform other state-of-arts on most of the data sets and achieve comparable results on the rest of data sets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638325 | ICASSP |
Keywords | Field | DocType |
optimisation,radial basis function networks,face recognition,learning (artificial intelligence),knn,feature vector space,face recognition data sets,metric learning,metric based gaussian kernel learning,gaussian kernel,classification,machine learning,riemannian manifold,multiple kernel learning,mahalanobis distance type metric,kernel,support vector machines,vectors,measurement,learning artificial intelligence | Online machine learning,Instance-based learning,Stability (learning theory),Semi-supervised learning,Pattern recognition,Active learning (machine learning),Radial basis function kernel,Computer science,Metric (mathematics),Artificial intelligence,Kernel method,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Guo | 1 | 512 | 39.61 |
| Z. Jane Wang | 2 | 0 | 0.68 |