Name
Abstract | ||
|---|---|---|
In this article, a new kernel estimation method is introduced using the epigraph set of the l(1)-nonn. The new method produces a high-resolution and cross-term free estimates for Cohen's Class of Time-frequency (TF) distributions. The kernel estimation process starts with an initial rough TF distribution. This initial estimate is orthogonally projected onto the epigraph set of the l(1) norm in TF domain. Epigraph set of the l(1) nom produces a sparse time-frequency distribution. Sparsity in TF domain leads to cross-term free TE distributions. Experimental results are presented and the TF distributions obtained with the estimated kernel are compared to those obtained with an optimized kernel. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Time-frequency distributions,Cohen's Class,L1-norm,sparsity |
Field | DocType | ISSN |
Kernel (linear algebra),Applied mathematics,Pattern recognition,Kernel embedding of distributions,Kernel principal component analysis,Time–frequency analysis,Artificial intelligence,Epigraph,Variable kernel density estimation,Mathematics,Kernel density estimation | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeynel Deprem | 1 | 1 | 1.38 |
| A. Enis Çetin | 2 | 871 | 118.56 |