Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. The heavy computational demands of using large order models dro...
In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its highresolution counterpart using...
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al...
A critical problem in cluster ensemble research is how to combine multiple clusterings to yield a final superior clustering result. Leveraging advanced graph partitioning techniqu...
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...