College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
College of Information and Electrical Engineering, China Agricultural University, Beijing, China; College of Science, China Agricultural University, Beijing, Haidian, 100083, China.
Neural Netw. 2023 Jul;164:631-644. doi: 10.1016/j.neunet.2023.05.004. Epub 2023 May 10.
Distance metric learning has been a promising technology to improve the performance of algorithms related to distance metrics. The existing distance metric learning methods are either based on the class center or the nearest neighbor relationship. In this work, we propose a new distance metric learning method based on the class center and nearest neighbor relationship (DMLCN). Specifically, when centers of different classes overlap, DMLCN first splits each class into several clusters and uses one center to represent one cluster. Then, a distance metric is learned such that each example is close to the corresponding cluster center and the nearest neighbor relationship is kept for each receptive field. Therefore, while characterizing the local structure of data, the proposed method leads to intra-class compactness and inter-class dispersion simultaneously. Further, to better process complex data, we introduce multiple metrics into DMLCN (MMLCN) by learning a local metric for each center. Following that, a new classification decision rule is designed based on the proposed methods. Moreover, we develop an iterative algorithm to optimize the proposed methods. The convergence and complexity are analyzed theoretically. Experiments on different types of data sets including artificial data sets, benchmark data sets and noise data sets show the feasibility and effectiveness of the proposed methods.
距离度量学习是一种很有前途的技术，可以提高与距离度量相关的算法的性能。现有的距离度量学习方法要么基于类中心，要么基于最近邻关系。在这项工作中，我们提出了一种新的基于类中心和最近邻关系的距离度量学习方法（DMLCN）。具体来说，当不同类别的中心重叠时，DMLCN 首先将每个类划分为几个簇，并使用一个中心来表示一个簇。然后，学习一种距离度量，使得每个样本都接近相应的簇中心，并保持每个感受野的最近邻关系。因此，在描述数据的局部结构的同时，该方法同时导致类内紧凑性和类间离散性。此外，为了更好地处理复杂数据，我们通过为每个中心学习局部度量，将多个度量引入到 DMLCN（MMLCN）中。之后，基于所提出的方法设计了一种新的分类决策规则。此外，我们开发了一种迭代算法来优化所提出的方法。从理论上分析了收敛性和复杂性。在包括人工数据集、基准数据集和噪声数据集在内的不同类型数据集上的实验表明了所提出的方法的可行性和有效性。
