The invention discloses a semi-pairing multi-view neighborhood correlation analysis method. The method comprises the following steps: 1) calculating a similarity matrix of samples in views by using a k neighborhood and a radial basis function; 2) calculating a similarity matrix of samples among different views by utilizing a similarity matrix of paired samples shared by the samples among the views and the samples in the views; 3) constructing an optimization model of semi-pairing multi-view neighborhood correlation analysis, and converting the optimization model into a generalized eigenvalue problem by using a Lagrange multiplier method; 4) solving a generalized eigenvalue problem; 5) using the projection matrix of each view to carry out dimensionality reduction on the training sample and the test sample to obtain low-dimensional representations of the training sample and the test sample; and 6) identifying the test data after dimension reduction by using a k-nearest neighbor classifier, and calculating an identification rate. Not only can the correlation between paired multi-view samples be revealed, but also the information contained in a large number of unpaired samples can be effectively utilized, so that the over-fitting phenomenon caused by the limited number of the paired samples can be effectively relieved.
Semi-pairing multi-view neighborhood correlation analysis method