IMAT: matrix learning machine with interpolation mapping

In matrix learning, vector patterns are simply transformed into matrix ones by some reshaping techniques such as from 100 × 1 to 20 × 5. Unfortunately, the techniques are random and fail in some cases. To this end, a matrix learning machine with interpolation mapping named IMAT for short is proposed. IMAT interpolates each feature of the original vector pattern into its corresponding k-means slots so as to generate a matrix pattern with more structural information. Furthermore, the pairwise information of every two features can be introduced into the IMAT. After that, the IMAT can be applied into matrix-based classifiers. The contributions of the proposed IMAT are listed as follows. (i) the IMAT can extract more intrinsic structural information compared with those random techniques reshaping the vector into a matrix. (ii) The IMAT is supposed to be reasonably and naturally embedded into matrix-based classifiers. In the experiments, the authors´ previous work is adopted, a matrix-based classifier named MatMHKS, to examine the IMAT on some UCI datasets. The results verify the superior classification performance of IMAT.