论文标题
基于仿射非负协作表示模式分类
Affine Non-negative Collaborative Representation Based Pattern Classification
论文作者
论文摘要
在过去的十年中，基于表示形式的分类方法在模式识别中受到了很大的关注。特别是，据报道，最近提出的基于非负表性的分类（NRC）方法在广泛的分类任务中取得了有希望的结果。但是，NRC有两个主要缺点。首先，NRC的制定中没有正规化项，这可能导致不稳定的解决方案和错误分类。其次，NRC忽略了数据通常位于多个仿射子空间的结合，而不是实际应用中的线性子空间。为了解决上述问题，本文介绍了用于模式分类的仿射非负协作表示（ANCR）模型。更具体地说，ANCR对编码向量施加了正规化项。此外，ANCR引入了仿射约束，以更好地表示仿射子空间的数据。几个基准测试数据集的实验结果证明了所提出的ANCR方法的优点。我们ANCR的源代码可在https://github.com/yinhefeng/ancr上公开获得。
During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://github.com/yinhefeng/ANCR.
