Abstract
This letter presents a new transform learning (TL) based multisensor fusion framework referred to as TransFuse. Unlike the standard representation learning based techniques, TransFuse learns individual transforms for each sensor and fuses them using a common transform representation within a joint optimization formulation. Considering regression as a use case, both the non-kernelized and kernelized versions are presented; the solution steps for learning the transforms, coefficients, and the regression weights are provided. The performance of the proposed TransFuse is evaluated using two real-life datasets and comparisons with the standard well-known TL and dictionary learning techniques for regression are presented. The results demonstrate the superior performance of TransFuse compared to its counterparts and also show the importance of multisensor fusion.
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.
