Graph Regularized Tensor

Graph regularized tensors combine the power of tensor representations for high-dimensional data with the ability of graph structures to encode relationships between data points. Current research focuses on developing efficient algorithms, such as those leveraging tensor train decompositions and hypo-elliptic diffusions, to handle the computational challenges associated with large-scale tensor operations and incorporate graph information for improved model performance and interpretability. This approach finds applications in diverse fields like multi-view clustering, visual data completion, and financial modeling, offering advantages in handling complex data structures and incorporating domain knowledge for enhanced accuracy and reduced computational costs. The resulting models often exhibit improved performance and interpretability compared to traditional methods.