Apr 27, 2016
My research interest lies in the interaction of human-centered AI and healthcare.
In classification tasks, classifiers trained with finite examples might generalize poorly to new data with unknown variance. For this …
Junyuan Hong, Yang Li, Huanhuan Chen
In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of subspaces in learning processes to obtain more robust classifiers.
Junyuan Hong, Huanhuan Chen, Feng Lin