GP-LVM for Data Consolidation
published: Dec. 20, 2008, recorded: December 2008, views: 485
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.
Manymachine learning task are involvedwith the transfer of information fromone representation to a corresponding representation or tasks where several different observations represent the same underlying phenomenon. A classical algorithm for feature selection using information from multiple sources or representations is Canonical Correlation Analysis (CCA). In CCA the objective is to select features in each observation space that are maximally correlated compared to dimensionality reduction where the objective is to re-represent the data in a more efficient form. We suggest a dimensionality reduction technique that builds on CCA. By extending the latent space with two additional spaces, each specific to a partition of the data, the model is capable of representing the full variance of the data. In this paper we suggest a generative model for shared dimensionality reduction analogous to that of CCA.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !