Metric-Constrained Kernel Union of Subspaces


This paper addresses the problem of learning a collection of nonlinear manifolds. Inspired by kernel methods, it puts forth a generalization of the kernel subspace model, termed the Metric-Constrained Kernel Union-of-Subspaces (MC-KUoS) model. It then develops an iterative method for learning of an MC-KUoS whose solution is based on the data representation capability of the manifolds and distances between subspaces in the kernel (feature) space. The proposed method (when using Gaussian and polynomial kernels) outperforms existing competitive state-of-the-art methods for real-world image denoising, which shows the benefits of the MC-KUoS model and the proposed denoising approach.

DOI: 10.1109/ICASSP.2015.7179079

Extracted Key Phrases

1 Figure or Table

Cite this paper

@article{Wu2015MetricConstrainedKU, title={Metric-Constrained Kernel Union of Subspaces}, author={Tong Wu and Waheed Uz Zaman Bajwa}, journal={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2015}, pages={5778-5782} }