Multiple view learning based on tabular data

Comparing with single-view learning algorithm, multi-view learning algorithm has more powerful classification performance. However, multi-view learning algorithm needs multiple source patterns. Features of those multiple source information must satisfy with some independent conditions. In most real world case, it is easier for us to gain single source patterns. So it will be necessary for us to design multi-view learning algorithms that are on the basis of single source patterns. In our previous research, we proposed a multi-view learning algorithm which is named MultiV-MHKS and found that MultiV-MHKS can efficiently improve the recognition rate in multi-view learning. In the paper, on the basis of MultiV-MHKS, we propose a novel classification method named MultiV-TMHKS, which adopts the tabularized data technique to matrixize single source patterns. By this multiviewization approach we can gain different kinds of matrixes that are used in different views and then design proper sub-classifiers in corresponding views. We come up with a new matrixizing method for multiple view learning which is based on single source patterns.