Name
Abstract | ||
|---|---|---|
A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators. (C) 2015 Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.08.021 | NEUROCOMPUTING |
Keywords | Field | DocType |
Probability density function,Kernel density estimator,Sparse modeling,Minimum integrated square error | Multivariate kernel density estimation,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Variable kernel density estimation,Mathematics,Machine learning,Kernel regression,Kernel density estimation,Kernel (statistics) | Journal |
Volume | ISSN | Citations |
173 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| X Hong | 1 | 216 | 19.36 |
| Sheng Chen | 2 | 1035 | 111.98 |
| Victor M. Becerra | 3 | 100 | 17.58 |