Paired vector projection data classification method and system based on semi-supervised learning

The invention relates to a paired vector projection data classification method and system based on semi-supervised learning, and the method comprises the steps: building an adjacent graph according totwo types of training data, solving a Laplace matrix, and substituting the Laplace matrix into a Laplace manifold regular term; respectively calculating a positive class Laplace manifold regular term, a negative class Laplace manifold regular term, an intra-class divergence matrix of positive class data, an intra-class divergence matrix of negative class data, a positive inter-class divergence matrix and a negative inter-class divergence matrix; obtaining an optimal problem according to the data, and performing solving to obtain two optimal projection vectors; and projecting the label-free data to a high-dimensional space through a kernel function, projecting the two optimal projection vectors to two different subspaces, and respectively calculating the distances from the two optimal projection vectors to the cen