Multi-scale kernel basis and iterative orthogonal matching pursuit for sparse approximation

Autor:	Yang-Yang Wu, Songcan Chen, Duan-Sheng Chen, Zhi-Peng Xie
Rok vydání:	2009
Předmět:	Mathematical optimization Approximation theory Spline (mathematics) Iterative method Approximation algorithm Sparse approximation Invariant (mathematics) Matching pursuit Hermitian matrix Algorithm Mathematics
Zdroj:	2009 International Conference on Machine Learning and Cybernetics.
DOI:	10.1109/icmlc.2009.5212275
Popis:	Function basis and approximation algorithm are two key elements in sparse representation. In this paper, some cardinal spline kernel basis and translation invariant fast decreasing kernel basis are presented, an iterative orthogonal matching pursuit algorithm(IOMP) is proposed, which is based on iterative update of hermitian inverse matrix. Experiments and comparisons on sparse representation of signal and regression datasets demonstrate that the proposed multi-scale kernel basis and iterative orthogonal matching pursuit algorithm (IOMP) are good at fast computing sparse approximation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ea48323387e5858b9eddcd0454fb936c https://doi.org/10.1109/icmlc.2009.5212275 Zobrazit plný text záznamu