Abstract
A method using copula techniques to capture the dependence structure inside the independent feature subspaces is proposed in this paper. It differs from the previous approach that simply use the norm of the projection of visual data on the invariant feature subspace to give the probability density inside the independent subspaces. By modelling the independent feature subspaces with Archimedean copula and utilizing the relationship between Archimedean copula and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm symmetric distribution, we make use of the corresponding radial distribution as the feature information to process feature extraction.
Sign-in/Register to access full text options 
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.
