Co-regularized Spectral Clustering with Multiple Kernels
published: Jan. 12, 2011, recorded: December 2010, views: 4643
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.
We propose a co-regularization based multiview spectral clustering algorithm which enforces the clusterings across multiple views to agree with each-other. Since each view can be used to define a similarity graph over the data, our algorithm can also be considered as learning with multiple similarity graphs, or equivalently with multiple kernels. We propose an objective function that implicitly combines two (or more) kernels, and leads to an improved clustering performance. Experimental comparisons with a number of baselines on several datasets establish the efficacy of our proposed approach.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !