Name
Abstract | ||
|---|---|---|
Lasso regression tends to assign zero weights to most irrelevant or redun- dant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches re- quire to solve hard non-convex optimization problems. This paper pro- poses a new approach named the Feature Vector Machine (FVM). It re- formulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM gener- ates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete. |
Year | Venue | Keywords |
|---|---|---|
2005 | NIPS | feature space,feature vector,kernel machine,linear model,feature selection |
Field | DocType | Citations |
Dimensionality reduction,Feature selection,Computer science,Lasso (statistics),Feature (machine learning),Artificial intelligence,Mathematical optimization,Feature vector,Pattern recognition,Feature (computer vision),Feature scaling,Kernel method,Machine learning | Conference | 30 |
PageRank | References | Authors |
1.26 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Li | 1 | 30 | 1.60 |
| Yiming Yang | 2 | 3299 | 344.91 |
| Bo Xing | 3 | 7332 | 471.43 |