Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned e...
We present an algorithm, Hierarchical ISOmetric SelfOrganizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimension...
We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank represe...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M partially labeled data mani...