Unsupervised Classification of Multiple Attributes via Autoassociative Neural Network
Reina Kamioka, Kouji Kurata, Kazuyuki Hiraoka, Taketoshi Mishima · ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications · 2002
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).