Name
Title | ||
|---|---|---|
Regression based D-optimality experimental design for sparse kernel density estimation |
Abstract | ||
|---|---|---|
This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.neucom.2009.11.002 | Neurocomputing |
Keywords | Field | DocType |
sparse kernel density,parzen window estimate,significant kernel,kernel selection task,accurate kernel weight estimate,sparse kernel density estimate,probability density function,d-optimality experimental design,orthogonal forward regression,kernel training data,d-optimality,optimal experimental design,sparse kernel modelling,kernel density estimate,kernel design matrix,small subset,sparse kernel density estimation,kernel density,quadratic program | Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Variable kernel density estimation,Mathematics,Kernel regression,Machine learning,Kernel (statistics),Kernel density estimation | Journal |
Volume | Issue | ISSN |
73 | 4-6 | Neurocomputing |
Citations | PageRank | References |
3 | 0.43 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Chen | 1 | 122 | 10.19 |
| X. Hong | 2 | 157 | 11.12 |
| C. J. Harris | 3 | 132 | 7.59 |