Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and Separability in Tensor Complexity and Model Capacity
It addresses theoretical modeling capacity problems in tensor analysis, which is incremental as it builds on existing concepts of tensor ranks and separability.
This study clarifies criteria for assessing tensor model capacity by analyzing tensor ranks and introducing separability issues, establishing a connection between information theory entanglement and tensor analysis to shed new light on theoretical understanding.
This study clarifies the proper criteria to assess the modeling capacity of a general tensor model. The work analyze the problem based on the study of tensor ranks, which is not a well-defined quantity for higher order tensors. To process, the author introduces the separability issue to discuss the Cannikin's law of tensor modeling. Interestingly, a connection between entanglement studied in information theory and tensor analysis is established, shedding new light on the theoretical understanding for modeling capacity problems.