Non-negativity constraints on the pre-image for pattern recognition with kernel machines

作者：

Highlights：

• The pre-image problem for pattern recognition.

• We study a constrained pre-image problem with non-negativity constraints.

• New theoretical results on the pre-image problem, including conditions for the convexity of the preimage problem.

• A fortuitous side-effect of our method is the sparsity in the representation, a property investigated in this paper.

摘要

•The pre-image problem for pattern recognition.•We study a constrained pre-image problem with non-negativity constraints.•New theoretical results on the pre-image problem, including conditions for the convexity of the preimage problem.•A fortuitous side-effect of our method is the sparsity in the representation, a property investigated in this paper.

论文关键词：Kernel machines,Machine learning,SVM,Kernel PCA,Pre-image problem,Non-negativity constraints,Nonlinear denoising,Pattern recognition

论文评审过程：Received 4 April 2012, Revised 8 March 2013, Accepted 29 March 2013, Available online 10 April 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.03.021