Feature map learning with partial training data

Summary form only given, as follows. The authors discuss a straightforward extension of the Kohonen self-organizing feature map that permits training and operation with incomplete training examples-input vectors in which values for some elements are missing. The matching and weight updating process is performed in the input subspace defined by the available input values. Three examples demonstrated the effectiveness of the extension