Title :
A self-organized growing network for on-line unsupervised learning
Author :
Furao, Shen ; Hasegawa, Osamu
Author_Institution :
Dept. of Comput., Tokyo Inst. of Technol., Japan
Abstract :
An on-line unsupervised learning mechanism is proposed for unlabeled data which is polluted by noises. By using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. The definition of a utility parameter - "error-radius" enables this system to learn the number of nodes needed to solve a current task. The usage of a new technique for removing nodes in low probability density regions can separate the clusters with low-density overlaps and dynamically eliminate the noise in the input data. The design of two-layer neural network makes it possible for 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 any priori conditions such as suitable number of nodes or a good initial codebook.
Keywords :
noise; pattern clustering; self-organising feature maps; unsupervised learning; local error based insertion criterion; online nonstationary data distribution; pattern clustering; probability density regions; self-organized growing network; two-layer neural network; unlabeled data; unsupervised learning; Clustering algorithms; Competitive intelligence; Intelligent systems; Learning systems; Neural networks; Partitioning algorithms; Pollution; Prototypes; Topology; Unsupervised learning;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1379860