Name
Title | ||
|---|---|---|
Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget. |
Abstract | ||
|---|---|---|
Kernel-based methods enjoy powerful generalization capabilities in learning a variety of pattern recognition tasks. When such methods are provided with sufficient training data, broadly applicable classes of nonlinear functions can be approximated with desired accuracy. Nevertheless, inherent to the nonparametric nature of kernel-based estimators are computational and memory requirements that beco... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2018.2802446 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Kernel,Feature extraction,Task analysis,Complexity theory,Memory management,Approximation algorithms,Heuristic algorithms | Kernel (linear algebra),Approximation algorithm,Mathematical optimization,Subspace topology,Feature extraction,Memory management,Artificial intelligence,Kernel method,Mathematics,Machine learning,Kernel (statistics),Generative model | Journal |
Volume | Issue | ISSN |
66 | 8 | 1053-587X |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatemeh Sheikholeslami | 1 | 52 | 5.35 |
| Dimitris Berberidis | 2 | 45 | 7.47 |
| Georgios B. Giannakis | 3 | 4977 | 340.58 |