完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Qiu, Jun-Wei | en_US |
dc.contributor.author | Zao, John K. | en_US |
dc.contributor.author | Chou, Yu-Hsiang | en_US |
dc.date.accessioned | 2014-12-08T15:29:04Z | - |
dc.date.available | 2014-12-08T15:29:04Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.isbn | 978-1-4673-1509-8 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/20974 | - |
dc.description.abstract | This paper presents a memetic algorithm, christened the natural memetic pursuit (NMP), that was designed to search for sparse signal representations. This algorithm combines global sampling based on a Grassmannian dictionary of atomic signals with local search using natural gradient decent in the signal parameter space. Performance of the algorithm was demonstrated by analyzing Jung and Makig's ERP data sets. It can obtain unbiased sparse representations of EEG signals in far less iteration than its predecessor, the stochastic matching pursuit (SMP). | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Memetic Algorithm | en_US |
dc.subject | Natural Gradient | en_US |
dc.subject | Grassmannian Gabor Dictionaries | en_US |
dc.subject | Sparse Signal Representation | en_US |
dc.subject | EEG Signal Processing | en_US |
dc.title | A Geometrically Faithful Memetic Algorithm for Searching Sparse Representations of EEG Signals | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000312859302077 | - |
顯示於類別: | 會議論文 |