Advanced Applications of Tensor Neural Network for High-Dimension Data Processing and Analysis

Dear Colleagues,

Tensor networks are mathematical tools for representing high-dimensional data, often used in the fields of quantum information and machine learning. Based on the concept of tensors, they represent multi-dimensional data as a product of tensors and reduce dimensionality and extract features by merging and decomposing these tensors.

In the aspect of image processing, tensor networks can efficiently extract features and reduce the dimensions of images, thus reducing the number of model parameters and improving the generalization ability and training speed of the model. In natural language processing, tensor networks are also widely used in text classification, machine translation, and other tasks. In addition, tensor networks also have important applications in the fields of recommendation system, object recognition, speech recognition, etc. The aim of this Special Issue is to attract researchers in these areas in order to reflect the recent advances in high-dimension data processing and analysis, both from a theoretical and an applied point of view.

The topics of interest for this Special Issue include (but are not limited to) the following:

1. Theoretical foundations of tensor decomposition and reconstruction;
2. High-dimensional data processing and analysis theory;
3. Machine learning based on tensor network;
4. Tensor neural network optimization;
5. Tensors in hyperspectral image tasks; 
6. Tensors in long-term time series prediction;
7. Tensors in medical image tasks;
8. Matrices and tensors in image restoration.

Prof. Dr. Hu Zhu
Dr. Xiongjun Zhang
Prof. Dr. Yansheng Li
Guest Editors

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• high dimension
• neural network
• tensor
• data analysis