一种具有动态邻域特点的自适应最近邻居算法

传统的最近邻居算法主要分为k-最近邻居和逆最近邻居,然而二者均在邻域参数选择问题中饱受困扰。在这两种思想的基础上,提出 一种具有动态邻域特点的最近邻居算法——自然邻居,并围绕其概念与特性形成了一套有效的方法。该算法从根本上克服了传统最近邻居思想在任意形状(如流型)数据集中参数选择的难题,摆脱了传统方法的参数依赖,并且取得了极佳的效果。自然邻居思想具有完善的理论模型和详细的实现算法,并且经验证其具有很强的鲁棒性和适应性。

Adaptive Nearest Neighbor Algorithm with Dynamic Neighborhood

Traditional nearest neighbor algorithm includes k-nearest neighbor (KNN) and reverse nearest neighbor (RNN),and they have been proposed in the literature,but most of them are vulnerable to their parameter choice.In this paper,a novel algorithm of nearest neighbor was proposed,named natural neighbor (NaN).In contrast to KNN and RNN,it is a scale-free nearest neighbor,and it can be used in any dataset effectually,especially data on manifold.This article discussed the theoretical model and its detailed implementation algorithm of natural neighbor in a different field,and the related questions of NaN concepts were discussed by the experimental tests.