Learning Vector Quantization with adaptive prototype addition and removal
Learning Vector Quantization with adaptive prototype addition and removal
复制标题
通过自适应原型添加和删除来学习矢量量化
DOI：
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发表时间：
2009
期刊：
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通讯作者：
S. Vucetic
中科院分区：
文献类型：
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作者：
Mihajlo Grbovic;S. Vucetic
学习矢量量化(LVQ)是一类流行的多类分类的最近原型分类器。这一系列的学习算法由于其直观清晰的学习过程和易于实现而被广泛使用。它们的运行效率很高，在许多情况下提供最先进的性能。在本文中，我们提出了一种改进的LVQ算法，解决了确定合适的原型数量、对初始化的敏感度和对数据中的噪声的敏感度的问题。所提出的算法允许在潜在有益的位置自适应地添加原型，并移除有害或不太有用的原型。原型添加和删除步骤可以很容易地在许多现有的LVQ算法上实现。在人工合成数据集和基准数据集上的实验结果表明，所提出的改进算法在确定合适的原型个数和避免初始化问题的同时，显著提高了LVQ分类精度。
Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. They run efficiently and in many cases provide state of the art performance. In this paper we propose a modification of the LVQ algorithm that addresses problems of determining appropriate number of prototypes, sensitivity to initialization, and sensitivity to noise in data. The proposed algorithm allows adaptive addition of prototypes at potentially beneficial locations and removal of harmful or less useful prototypes. The prototype addition and removal steps can be easily implemented on top of many existing LVQ algorithms. Experimental results on synthetic and benchmark datasets showed that the proposed modifications can significantly improve LVQ classification accuracy while at the same time determining the appropriate number of prototypes and avoiding the problems of initialization.