대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

Unsupervised Classiflcation of Multiple Attributes via Autoassociative Neural Network

  • Kamioka, Reina (Dept. of Information and Computer Science, Saitama University) ;
  • Kurata, Kouji (Dept. of Mechanical System Engineering, Ryukyu University) ;
  • Hiraoka, Kazuyuki (Dept. of Information and Computer Science, Saitama University) ;
  • Mishima, Taketoshi (Dept. of Information and Computer Science, Saitama University)
  • 발행 : 2002.07.01
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초록

This paper proposes unsupervised classification of multiple attributes via five-layer autoassociative neural network with bottleneck layer. In the conventional methods, high dimensional data are compressed into low dimensional data at bottleneck layer and then feature extraction is performed (Fig.1). In contrast, in the proposed method, analog data is compressed into digital data. Furthermore bottleneck layer is divided into two segments so that each attribute, which is a discrete value, is extracted in corresponding segment (Fig.2).

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