NDLI: An on-line learning mechanism for unsupervised classification and topology representation

Content Provider	IEEE Xplore Digital Library
Author	Furao, S. Hasegawa, O.
Copyright Year	2005
Description	Author affiliation: Tokyo Inst. of Technol., Japan (Furao, S.; Hasegawa, O.)
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.
Sponsorship	IEEE Comput. Soc
Starting Page	651
Ending Page	656
File Size	1030416
Page Count	6
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Topology Computer Society Computer vision Pattern recognition Erbium
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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