Abstract
The author explores Householder transforms and their applications in signal processing. He shows that these transforms can be viewed as mirror-image reflections of a data vector about any desired hyperplane. The virtue of reflections is that they are covariance invariant, that is, they preserve the covariance matrix of the data. One can construct a finite sequence of such reflections that maps a block of data vectors into a lower rectangular matrix. If only the covariance eigenvalues need to be preserved, one can map into a bidiagonal matrix. The former sparse form is useful for inverting covariance matrices and the latter is useful in finding eigenvalues of covariance matrices.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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.
