Injection of external information to feature maps of multiply descent cost competitive learning
Yasuo Matsuyama, Yoshiaki Kurosawa · 1991
Multiple descent cost competitive learning simultaneously generates two types of feature maps by self-organization. One is a grouped pattern of atomic data elements; the other is a geometric structure on the set of neural weight vectors. In the case of images, the grouped pattern is a set of nonoverlapping quadrilaterals. Each quadrilateral is associated with a neural weight vector, i.e., an image patch. Then, control of the grouped pattern based on external intelligence creates new images. By this method, generation of new emotional features on facial images is attempted. Thus, the feature map of the multiple descent cost competitive learning is not used for recognition but is utilized for creation of new patterns by incorporating additional information.>