Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …

Many critical electronic design automation (EDA) problems suffer from the curse of
dimensionality, ie, the very fast-scaling computational burden produced by large number of …

This paper revisits the problem of finding the best rank-1 approximation to a symmetric
tensor and makes three contributions. First, in contrast to the many long and lingering …

While every matrix admits a singular value decomposition, in which the terms are pairwise
orthogonal in a strong sense, higher-order tensors typically do not admit such an orthogonal …

We generalize the matrix Kronecker product to tensors and propose the tensor Kronecker
product singular value decomposition that decomposes a real k‐way tensor into a linear …

Channel neural decoding is very promising as it outperforms the traditional channel
decoding algorithms. Unfortunately, it still faces the disadvantage of high computational …

Inferences of adaptive events are important for learning about traits, such as human
digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward …

In this study, the authors study the problem of tensor completion, in particular for three‐
dimensional arrays such as visual data. Previous works have shown that the low‐rank …