K-SVD dictionary learning using a fast OMP with applications

Autor:	Mahmood R. Azimi-Sadjadi, Justin Kopacz, Nick Klausner
Rok vydání:	2014
Předmět:	Set (abstract data type) Matrix (mathematics) K-SVD Computer science business.industry Orthographic projection Pattern recognition Artificial intelligence business Matching pursuit Conjunction (grammar) Interpretation (model theory)
Zdroj:	ICIP
DOI:	10.1109/icip.2014.7025320
Popis:	K-SVD method has recently been introduced to learn a specific dictionary matrix that best fits a set of training data vectors. K-SVD is flexible in that any preferred pursuit method of sparse coding can be used to represent the data. In this paper, we show how K-SVD method can be used in conjunction with a fast orthogonal matching pursuit implemented using orthogonal projection updating. Geometric interpretation of this learning is also presented. The method was then applied to underwater target detection problem using a dual-channel sonar imagery data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fd644ff067081947f6ae4ca95917cf2d https://doi.org/10.1109/icip.2014.7025320 Zobrazit plný text záznamu