Metric-Constrained Kernel Union of Subspaces
Metric-Constrained Kernel Union of Subspaces
复制标题
子空间的度量约束核并
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
W. Bajwa
中科院分区：
文献类型：
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作者：
Tong Wu;W. Bajwa
本文讨论了学习一组非线性流形的问题。受核方法的启发，提出了核子空间模型的推广，称为度量约束核子空间并集（MC-KUoS）模型。然后，它开发了一种迭代方法来学习MC-KUoS，其解决方案基于流形的数据表示能力和核（特征）空间中子空间之间的距离。所提出的方法（当使用高斯和多项式核函数时）在现实世界的图像去噪方面优于现有的竞争最先进的方法，这表明了MC-KUoS模型和所提出的去噪方法的优点。
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.