/block-term-tensor-regression

A new generalized multilinear regression model, termed the Block-Term Tensor Regression (BTTR), is introduced with the aim to predict a tensor (multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the data onto the latent space and performing regression on the corresponding latent variables.

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Block-Term Tensor Regression (BTTR)

BTTR is a deflation-based method in which the maximally correlated representations of X and Y are extracted via ACE/ACCoS (Automatic Component Extraction / Automatic Correlated Component Selection) at each iteration. Therefore, BTTR inherits the advantages of the proposed ACE/ACCoS and does not require one to set the model parameters manually. This provides BTTR with an additional important property: the ability to model complex data in which the optimal Multilinear Rank (MTR) is not necessarily stable across sequential decompositions.

[1] Faes, Axel, Flavio Camarrone, and Marc M. Van Hulle. "Single finger trajectory prediction from intracranial brain activity using block-term tensor regression with fast and automatic component extraction." IEEE Transactions on Neural Networks and Learning Systems (2022).