An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation
Abstract
An on-line learning mechanism is proposed for unsupervised data. Using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of online 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 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 noise in the input data. Experiment results show that this system can report a reasonable number of clusters and represent the topological structure of unsupervised on-line data with no prior conditions such as a suitable number of nodes or a good initial codebook.
Cite
Text
Furao and Hasegawa. "An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.69Markdown
[Furao and Hasegawa. "An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/furao2005cvpr-line/) doi:10.1109/CVPR.2005.69BibTeX
@inproceedings{furao2005cvpr-line,
title = {{An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation}},
author = {Furao, Shen and Hasegawa, Osamu},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {651-656},
doi = {10.1109/CVPR.2005.69},
url = {https://mlanthology.org/cvpr/2005/furao2005cvpr-line/}
}