An incremental network for on-line unsupervised classification and topology learning
An incremental network for on-line unsupervised classification and topology learning
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DOI：
10.1016/j.neunet.2005.04.006
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发表时间：
2006-01-01
期刊：
影响因子：
7.8
通讯作者：
Hasegawa, O
中科院分区：
文献类型：
--
作者：
Shen, FR;Hasegawa, O
针对噪声污染的未标注数据，提出了一种在线无监督学习机制。使用基于相似度阈值和基于局部误差的插入准则，系统能够增量地增长并适应在线非平稳数据分布的输入模式。效用参数的定义，误差半径，允许该系统学习求解任务所需的节点数。使用一种新的技术来去除低概率密度区域中的节点，可以分离出低密度重叠的簇，并动态地消除输入数据中的噪声。两层神经网络的设计使得该系统能够表示非监督在线数据的拓扑结构，报告合理的聚类数目，并在没有预先条件的情况下给出每个聚类的典型原型模式，如合适的节点数目或良好的初始码本。(C)2005爱思唯尔有限公司。保留所有权利。
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity threshold-based and a local error-based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. A definition of a utility parameter, the error-radius, allows this system to learn the number of nodes needed to solve a task. The use of a new technique for removing nodes in low probability density regions can separate clusters with low-density overlaps and dynamically eliminate noise in the input data. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes or a good initial codebook. (c) 2005 Elsevier Ltd. All rights reserved.