The invention discloses a supervised multi-set correlation feature fusion method based on spectral reconstruction. The method comprises the following steps: 1) defining a projection direction of a training sample set; 2) calculating an inter-group intra-class correlation matrix and an auto-covariance matrix of the training sample; 3) performing singular value decomposition on inter-group intra-class correlation matrixes, and performing eigenvalue decomposition on auto-covariance matrixes; 4) reconstructing a fractional order inter-group intra-class correlation matrix and a fractional order auto-covariance matrix; 5) constructing an optimization model of the FDMCCA; 6) solving the feature vector matrix to form a projection matrix; 7) fusing the features after dimension reduction; and 8) seleceting different numbers of images to serve as a training set and a test set respectively, and calculating the recognition rate. According to the method, the information fusion problem of multiple view data can be effectively solved, meanwhile, the influence caused by noise interference and limited training samples is weakened through introduction of fractional order parameters, and the system recognition accuracy is improved.
Supervision multi-set correlation feature fusion method based on spectrum reconstruction