Greedy approaches to semi-supervised subspace learning

作者：

Highlights：

• A unifying optimization problem formulated for semi-supervised subspace learning.

• Nuclear-norm regularized optimization tackled by efficient inf-dim greedy search.

• Nonlinear kernel extension introduced with no extra computational complexity.

• Superior performance than existing methods on several interesting datasets.

摘要

Highlights•A unifying optimization problem formulated for semi-supervised subspace learning.•Nuclear-norm regularized optimization tackled by efficient inf-dim greedy search.•Nonlinear kernel extension introduced with no extra computational complexity.•Superior performance than existing methods on several interesting datasets.

论文关键词：Dimensionality reduction,Infinite-dim greedy search,Semi-supervised learning

论文评审过程：Received 18 July 2013, Revised 20 September 2014, Accepted 19 October 2014, Available online 31 October 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.031