Unsupervised learning neural network with convex constraint: Structure and algorithm

This paper proposed a kind of unsupervised learning neural network model, which has special structure and can realize an evaluation and classification of many groups by the compression of data and the reduction of dimension. The main characteristics of the samples were learned after being trained. In order to realize unsupervised learning of neural network structure with convex constraint, an iterative computation method is proposed that makes use of alternating projection between two convex sets. The final example proved that this method can detect instructions without a mass of supervised data and it converges fast. 1 Keywords. Neural network; Unsupervised learning; Convex constraint; Iterative algorithm by alternating projection.

• Type: Journal
• Year: 2008
• Where: IJON
• Authors: Hengqing Tong, Tianzhen Liu, Qiaoling Tong
• Keywords: Convex Constraint, IJON 2008, Neural Network, Unsupervised Learning