Abstract
In this paper we study extraction of independent components from the instantaneous sparse mixture with additive Gaussian noise. We model the problem as a dictionary-learning-like objective function which tries to discover independent atoms and corresponding sparse mixing matrix. The objective function involves fidelity term, L1 normalization term and Negentropy term which respectively limits noise, maximizes the sparseness of mixing matrix and non-Gaussianity of each atom. An alternative iteration algorithm is proposed to solve the optimization. According to our simulation, the proposed method outperforms FastICA and K-SVD.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
