Double-fold localized multiple matrix learning machine with Universum

Abstract

Matrix learning, multiple-view learning, Universum learning, and local learning are four hot spots of present research. Matrix learning aims to design feasible machines to process matrix patterns directly. Multiple-view learning takes pattern information from multiple aspects, i.e., multiple-view information into account. Universum learning can reflect priori knowledge about application domain and improve classification performances. A good local learning approach is important to the finding of local structures and pattern information. Our previous proposed learning machine, double-fold localized multiple matrix learning machine is a one with multiple-view information, local structures, and matrix learning. But this machine does not take Universum learning into account. Thus, this paper proposes a double-fold localized multiple matrix learning machine with Universum (Uni-DLMMLM) so as to improve the performance of a learning machine. Experimental results have validated that Uni-DLMMLM (1) makes full use of the domain knowledge of whole data distribution as well as inherits the advantages of matrix learning; (2) combines Universum learning with matrix learning so as to capture more global knowledge; (3) has a good ability to process different kinds of data sets; (4) has a superior classification performance and leads to a low empirical generation risk bound.