학술논문
Convolutional higher order matching pursuit
Document Type
Conference
Author
Source
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on. :1-6 Sep, 2016
Subject
Language
Abstract
We introduce a greedy generalised convolutional algorithm to efficiently locate an unknown number of sources in a series of (possibly multidimensional) images, where each source contributes a localised and low-dimensional but otherwise variable signal to its immediate spatial neighbourhood. Our approach extends convolutional matching pursuit in two ways: first, it takes the signal generated by each source to be a variable linear combination of aligned dictionary elements; and second, it executes the pursuit in the domain of high-order multivariate cumulant statistics. The resulting algorithm adapts to varying signal and noise distributions to flexibly recover source signals in a variety of settings.