Support Tensor Machine Image Classification Based on Weighted High-order Singular Value Decomposition

To improve the accuracy of image classification,fully use the structural information of the data,and compress the image data,first,third order tensor image features are constructed.Then,non-negative matrix factorization (NMF) is used for dimension reduction.A method of choosing agood starting point is proposed using two-dimensional principal component analysis (2DPCA),which uses the information of the image effectively.Next,in order to maintain the intrinsic structure of the manifold for tensor subspace,weighted matrix is derived according to the labels of images.Meanwhile the set of images is used to construct a fourth order tensor.A method for classification is proposed by using weighted high-order singular value decomposition for support tensor machine.The experimental results on two image databases show that the proposed can effectively improve the accuracy of image classification.