Unsupervised Learning / IEEE Press Series on Computational Intelligence (PDF)

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of
information in recent years, but the question of how such
information might be effectively harvested, archived, and analyzed
remains a monumental challenge--for the processing of such
information is often fraught with the need for conceptual
interpretation: a relatively simple task for humans, yet an arduous
one for computers.

Inspired by the relative success of existing popular research on
self-organizing neural networks for data clustering and feature
extraction, Unsupervised Learning: A Dynamic Approach
presents information within the family of generative,
self-organizing maps, such as the self-organizing tree map (SOTM)
and the more advanced self-organizing hierarchical variance map
(SOHVM). It covers a series of pertinent, real-world applications
with regard to the processing of multimedia data--from its
role in generic image processing techniques, such as the automated
modeling and removal of impulse noise in digital images, to
problems in digital asset management and its various roles in
feature extraction, visual enhancement, segmentation, and analysis
of microbiological image data.

Self-organization concepts and applications discussed
include:

* Distance metrics for unsupervised clustering

* Synaptic self-amplification and competition

* Image retrieval

* Impulse noise removal

* Microbiological image analysis

Unsupervised Learning: A Dynamic Approach introduces a
new family of unsupervised algorithms that have a basis in
self-organization, making it an invaluable resource for
researchers, engineers, and scientists who want to create systems
that effectively model oppressive volumes of data with little or no
user intervention.