Name
Abstract | ||
|---|---|---|
A new sparse kernel probability density function pdf estimator based on zero-norm constraint is constructed using the classical Parzen window PW estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/00207721.2011.564673 | Int. J. Systems Science |
Keywords | Field | DocType |
so-called zero-norm,density function pdf estimator,sparse probability density function,proposed pdf estimator,zero-norm constraint,zero-norm approximation,target function,approximate function,new sparse kernel probability,kernel weight,parzen window,quadratic program,probability density function,cross validation | Kernel (linear algebra),Density estimation,Mathematical optimization,Multiplicative function,Quadratic programming,Probability density function,Mathematics,Kernel (statistics),Estimator,Kernel density estimation | Journal |
Volume | Issue | ISSN |
43 | 11 | 0020-7721 |
Citations | PageRank | References |
5 | 0.47 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| X. Hong | 1 | 157 | 11.12 |
| S. Chen | 2 | 5 | 0.47 |
| C. J. Harris | 3 | 132 | 7.59 |