223 citations · 302 across the 13 of their papers we have counts for
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cs.LG2015★ 1 cited
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient
Mingkui Tan, Shijie Xiao, Junbin Gao +3
Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving this problem directly can b…
cs.LG2012★ 223 cited
Learning with Augmented Features for Heterogeneous Domain Adaptation
Lixin Duan, Dong Xu, Ivor Tsang
We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features…