Processing method for high order tensor data

#This application is based upon and claims priority to Chinese Patent Application No. CN201910022095.X, filed on Jan. 10, 2019, the entire contents of which are incorporated herein by reference.

#The present invention relates to the technical field of data processing, and particularly to a processing method for high order tensor data, which is mainly used for the processing of video sequence, RGB-D sequence and other data.

#With the development of science and technology, a large number of high-order tensor data emerge, such as video sequence, RGB-D sequence and so on. Because there are a lot of redundant information in high-order tensor data, it is often necessary to extract features or reduce dimensions before data analysis.

#FIG. 1 shows a schematic diagram of formula (1) according to the invention.

#FIG. 2 shows a graphic illustration of probabilistic recognition method according to the present invention.

#FIG. 3 shows a visual effect of the reconstruction of the invention on the extended Yale database.

#As shown as FIG. 1, in this processing method for high-order tensor data, the high-order tensor data are divided into three parts: the shared subspace component, the personality subspace component and the noise part; the shared subspace component and the personality subspace component respectively represent the high-order tensor data as a group of linear combination of the tensor base and the vector coefficient; the variational EM method is used to solve the base tensor and the vector coefficient; design a classifier to classify the test samples by comparing the edge distribution of samples.

#Compared with the traditional linear discriminant method, the present invention extracts the common feature and the individual feature directly from the tensor data in two spaces respectively. In each space, by the structural feature of the tensor, it constructs a representation way from the tensor data to the vector data, so as to extract the discriminant feature of the tensor sample, thus avoiding that the vectorization process of the image observation sample set damage the internal structure of the data, which is flexible and simple, which can extract the discriminant features of the tensor sample, simplify the large amount of redundant information in the high-order tensor data in the image observation sample set, and improve the image processing speed.

#Preferably, two dimensional model is assuming an observed data set y={Yij}i=1,j=1I,Ni, which consist of N images of I individuals and each individual has Ni images. Yij represents the jth image of the ith individual and it will be modeled as formula (1): Yij=×3hiT+×3gijT+M+Eij  (1) In this model, ×3 hiT or ×3 gijT can be regarded as the generalization of vector representation for a given sample, and are the factor tensors consisting of base matrices in shared subspace and individual subspace, respectively. ×3 represents the product of tensor and matrix (vector), hi∈K1 is a shared latent variable crossing all the images Yi1, . . . , YiNi of object/class i and gij∈K2 represents the coefficient of the jth sample of the ith object in individual space. M is the mean matrix of the data, suppose M=0. Eij is a stochastic noise term, each element in which follows the normal distribution (0, σ2).