Partial Information from Spectral Methods
published: Oct. 6, 2014, recorded: December 2013, views: 1934
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.
Spectral methods are satisfying in that they provide statistically consistent estimators. However, two issues still challenge their widespread adoption: they tend to be inaccurate in smaller data regimes, and they do not directly apply to richer classes of latent-variable models. In this talk, we show some initial progress on both of these issues by using spectral methods to obtain partial information about the parameters rather than using them as a standalone estimator. We also show preliminary results on learning log-linear and loopy graphical models.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !