Interpolation on data with multiple attributes by a neural network

High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, the authors propose a method to estimate a missing attribute data from other data. If this becomes possible, the pattern recognition of robust multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Begin, model [1] is a model that realizes the above key feature. However, experiments on application of bilinear model to classification of face images resulted in low performance [2]. Then, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.