Self-Adjusting Feature Maps Network

Chin-Teng Lin, Dong-Lin Li, Jyh-Yeong Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we propose a novel artificial neural network, called self-adjusting feature map (SAM), and its unsupervised learning algorithm with self-adjusting mechanism. After the training of SAM network, we will obtain a map composed of a set of representative connected neurons. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. SAM can automatically isolate a set of connected neurons, in which the number of the set may indicate the number of clusters to be used. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advance and retreat of waste. For each representative neuron, there are three periods, growth. adaptation and decline, in its training process. The network of representative neurons will first create the necessary neurons according to the local density of the input data in the growth period. Then it will adjust neighborhood neuron pair's connected/disconnected topology constantly according to the statistics of input feature data in the adaptation period. Lastly the unnecessary neurons of the network will be merged or deleted in the decline period. In this study. we exploit SAM to handle some peculiar cases that cannot be well dealt with by classical unsupervised learning networks such as self-organizing feature map (SOM) network. Furthermore, we also take several real world cases to exhibit the remarkable characteristics of SAM.
Original languageEnglish
PublisherSpringer-Verlag Berlin Heidelberg
Number of pages9
ISBN (Print)978-3-642-24964-8, 978-3-642-24965-5
StatePublished - 2011

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Unsupervised learning
  • self-adjusting
  • representative neurons statistics
  • SOM

Fingerprint Dive into the research topics of 'Self-Adjusting Feature Maps Network'. Together they form a unique fingerprint.

Cite this