Year-end Sale % OFF 
Abstract
A novel method called binNMF is introduced which aimed to extract hidden information from multivariate binary data sets. The method treats the problem in the spirit of blind source separation: The data are assumed to be generated by a superposition of several simultaneously acting sources or elementary causes which are not observable directly. The superposition process is based on a minimum of assumptions and reversed to identify the underlying sources. The method is motivated, developed, and demonstrated in the context of binary wafer test data which evolve during microchip fabrication.
Published Version
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 callMore From: IEEE Transactions on Circuits and Systems I: Regular Papers
Disclaimer: 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.
