Multitask Learning Using Nonparametrically Learned Predictor Subspaces
published: Jan. 19, 2010, recorded: December 2009, views: 4409
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.
Given several related learning tasks, we propose a nonparametric Bayesian learning model that captures task relatedness by assuming that the task parameters (i.e., weight vectors) share a latent subspace. More speciﬁcally, the intrinsic dimensionality of this subspace is not assumed to be known a priori. We use an inﬁnite latent feature model - the Indian Buffet Process - to automatically infer this number. We also propose extensions of this model where the subspace learning can incorporate (labeled, and additionally unlabeled if available) examples, or the task parameters share a mixture of subspaces, instead of sharing a single subspace. The latter property can allow learning nonlinear manifold structure underlying the task parameters, and can also help in preventing negative transfer from outlier tasks.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !