Twin Support Matrix Classification Machine Based on Singular Value Decomposition

Currently, the tensor, a form commonly seen, finds increasingly wider application in various kinds of fields.Matrix, as a second-order tensor, can be employed to bridge between a vector and a tensor. High order tensor can also be un- folded into matrix formulation. So, it is of vital significance to research into matrix-input-based classification problems. For matrix-input-based classification problems, based on multi-rank multi-linear twin support matrix classification machine, a twin- support matrix classification machine is built on the basis of singular value decomposition. A matrix projecting function is de- fined to handle matrix input on basis of matrix singular value decomposition, reducing the dimensions of matrix input and re- formulating a new training set. By learning the new training set, the classification accuracy improves and the training time de- creases. Five matrix data sets are then subjected to training and compared with other classification methods, the twin support matrix classification machine based on singular value decomposition is found to be an efficient classification machine.