標題: | Self-adjusting feature maps network and its applications |
作者: | Li, Dong-Lin Prasad, Mukesh Lin, Chin-Teng Chang, Jyh-Yeong 資訊工程學系 電子工程學系及電子研究所 Department of Computer Science Department of Electronics Engineering and Institute of Electronics |
關鍵字: | Unsupervised learning;Self-adjusting;Statistics;Quantization;Self-organizing map;Artificial neural networks |
公開日期: | 26-九月-2016 |
摘要: | This paper, proposes a novel artificial neural network, called self-adjusting feature map (SAM), and develop its unsupervised learning ability with self-adjusting mechanism. 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. The SAM can automatically isolate a set of connected neurons, in which, the used number of the sets may indicate the number of clusters. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advantages and retreat of waste. In the training process,, for each representative neuron has are three phases, growth, adaptation and decline. The network of representative neurons, first create the necessary neurons according to the local density of the input data in the growth phase. In the adaption phase, it adjusts neighborhood neuron pair\'s connected/disconnected topology constantly according to the statistics of input feature data. Finally, the unnecessary neurons of the network are merged or remove in the decline phase. In this paper, we exploit the SAM to handle some peculiar cases that cannot be handled easily by classical unsupervised learning networks such as self-organizing map (SOM) network. The remarkable characteristics of the SAM can be seen on various real world cases in the experimental results. (C) 2016 Elsevier B.V. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.neucom.2016.03.067 http://hdl.handle.net/11536/134058 |
ISSN: | 0925-2312 |
DOI: | 10.1016/j.neucom.2016.03.067 |
期刊: | NEUROCOMPUTING |
Volume: | 207 |
起始頁: | 78 |
結束頁: | 94 |
顯示於類別: | 期刊論文 |