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3) Robust sparse regression methods with outlier rejection.
3) Robust sparse regression methods with outlier rejection.
===Kernel Learning for Extrinsic Classification of Manifold Features===
Speaker: Raviteja Vemulapalli : May 9, 2013
For features that lie in Euclidean spaces, classifiers based on discriminative approaches such as linear discriminant analysis (LDA), partial least squares (PLS) and support vector machines (SVM) have been successfully used in various applications. However, these techniques are not directly applicable to features that lie on Riemannian manifolds. One possible solution to this problem is to define kernels on the manifolds. In this talk I will discuss about kernels and multiple kernel learning focusing on Grassmann manifold and the manifold of symmetric positive definite matrices.
===Adding Unlabeled Samples to Categories by Learned Attributes===
Speaker: [http://www.umiacs.umd.edu/~jhchoi/ Jonghyun Choi] : May 9, 2013
We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a subset of a large-scale dataset, ImageNet.
==Past Semesters==
==Past Semesters==
