An incremental network for on-line unsupervised classification and topology learning

This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line nonstationary 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. q 2005 Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Shen Furao, Osamu Hasegawa
• Keywords: Error-based Insertion Criterion, Neural Networks, NN 2006, On-line Nonstationary Data, On-line Unsupervised Learning