Abstract
We generalize cyclic matching pursuit (CMP),
propose an orthogonal variant,
and examine their performance using multiscale time-frequency dictionaries
in the sparse approximation of signals.
Overall, we find that the cyclic approach of CMP
produces signal models that have a much lower approximation error
than existing greedy iterative descent methods
such as matching pursuit (MP),
and are competitive with models found using orthogonal MP (OMP),
and orthogonal least squares (OLS).
This implies that CMP is a strong alternative to
the more computationally complex approaches of OMP and OLS
for modeling high-dimensional signals.
propose an orthogonal variant,
and examine their performance using multiscale time-frequency dictionaries
in the sparse approximation of signals.
Overall, we find that the cyclic approach of CMP
produces signal models that have a much lower approximation error
than existing greedy iterative descent methods
such as matching pursuit (MP),
and are competitive with models found using orthogonal MP (OMP),
and orthogonal least squares (OLS).
This implies that CMP is a strong alternative to
the more computationally complex approaches of OMP and OLS
for modeling high-dimensional signals.
| Original language | English |
|---|---|
| Journal | Asilomar Conference on Signals, Systems and Computers. Conference Record |
| Pages (from-to) | 581-585 |
| ISSN | 1058-6393 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 44th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States Duration: 7 Nov 2010 → 10 Nov 2010 |
Conference
| Conference | 44th Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Country/Territory | United States |
| City | Pacific Grove |
| Period | 07/11/2010 → 10/11/2010 |