Lecture 6: Least-Squares
published: May 31, 2010, recorded: September 2007, views: 4458
released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
So we're gonna talk about least squares. It's something you've probably seen in a couple of different contexts, and it concerns overdetermined linear equations. So we have a set of over determined linear equations. Now, here we have y=ax, where a is we'll make strictly skinny. It's overdetermined because you have more equations than unknowns. And, of course, unless y is in the range of a, which if you pick y randomly, and rm is an event of probability zero, you can't solve y=ax. So one method to approximately solve y=ax, and it's very important to emphasize here we're not actually solving y=ax, is to choose x to minimize the norm of this residual. ...
See the whole transcript at Introduction to Linear Dynamical Systems - Lecture 06
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !