Publication: Sparse bounded component analysis
Program
KU-Authors
KU Authors
Co-Authors
Babatas, Eren
Advisor
Publication Date
Language
English
Journal Title
Journal ISSN
Volume Title
Abstract
Bounded Component Analysis (BCA) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. This article introduces a novel deterministic instantaneous BCA approach for the separation of sparse bounded sources. The separation problem is posed as a geometric maximization problem, where the objective is the volume ratio of two geometric objects related to the separator output samples, namely the principal hyperellipsoid and bounding l1 norm ball. The global maxima of the corresponding objective are proven to be perfect separators. The article also provides an iterative algorithm corresponding to this objective. The numerical experiments illustrate the potential benefit of the proposed approach relative to existing algorithms.
Description
Source:
IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Publisher:
IEEE Computer Society
Keywords:
Subject
Engineering, Electrical electronic engineering