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Progressive feature extraction with a greedy network-growing algorithm. (English) Zbl 1167.68423

Summary: In this paper, a new information theoretic method called the greedy network-growing algorithm is proposed. The method is called “greedy,” because a network with this algorithm grows while absorbing as much information as possible from outside. The method is based upon information theoretic competitive learning and can solve the fundamental problems inherent in competitive learning, such as the dead neurons and inappropriate number of neurons problems. The new model can grow networks by repeatedly maximizing information content and by gradually extracting salient features from input patterns. Because the new model can cope with inappropriate feature detection in the early stage of learning, extracted features should cover most of the input patterns. The new method is applied to political data analysis, medical data analysis, and information science education analysis. Experimental results confirm that the new method can acquire significant information and that more explicit features can be extracted.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
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