  Weighted twin support vector machines (WLTSVM) mines as much potential
similarity information in samples as possible to improve the common
short-coming of non-parallel plane classifiers. Compared with twin support
vector machines (TWSVM), it reduces the time complexity by deleting the
superfluous constraints using the inter-class K-Nearest Neighbor (KNN).
Multi-view learning (MVL) is a newly developing direction of machine learning,
which focuses on learning acquiring information from the data indicated by
multiple feature sets. In this paper, we propose multi-view learning with
privileged weighted twin support vector machines (MPWTSVM). It not only
inherits the advantages of WLTSVM but also has its characteristics. Firstly, it
enhances generalization ability by mining intra-class information from the same
perspective. Secondly, it reduces the redundancy constraints with the help of
inter-class information, thus improving the running speed. Most importantly, it
can follow both the consensus and the complementarity principle simultaneously
as a multi-view classification model. The consensus principle is realized by
minimizing the coupling items of the two views in the original objective
function. The complementary principle is achieved by establishing privileged
information paradigms and MVL. A standard quadratic programming solver is used
to solve the problem. Compared with multi-view classification models such as
SVM-2K, MVTSVM, MCPK, and PSVM-2V, our model has better accuracy and
classification efficiency. Experimental results on 45 binary data sets prove
the effectiveness of our method.
