Name
Abstract | ||
|---|---|---|
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called Kernel Orthonomalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4422779 | IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET |
Keywords | Field | DocType |
scalability,approximation algorithms,remote sensing,sparse approximation,regression analysis,expressive power,least squares approximation,data mining,covariance matrix,kernel,feature extraction | Data mining,Data set,Radial basis function kernel,Computer science,Remote sensing,Kernel principal component analysis,Artificial intelligence,Kernel (linear algebra),Pattern recognition,Kernel embedding of distributions,Sparse approximation,Feature extraction,Scalability | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.39 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Arenas-Garc'ia | 1 | 1 | 0.39 |
| Camps-Valls, G. | 2 | 441 | 29.69 |