Abstract
To reduce computational complexity of the Linde-Buzo-Gray (LBG) algorithm, an improved fast training method in the Hadamard domain is proposed. This method uses an optimally-ordered Hadamard transform kernel based on the statistical energy distribution of transformed training vectors in k-dimensional space and a three-step elimination criterion to more efficiently reject impossible codevectors. Experimental results demonstrate the effectiveness of the proposed method in terms of arithmetical operations and search space.
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.
