Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor
data representation. To enhance the representation ability of NTD by multiple intrinsic cues …

With the advancement of data acquisition techniques, tensor (multidimensional data) objects
are increasingly accumulated and generated, for example, multichannel …

Tensor-ring (TR) decomposition is a powerful tool for exploiting the low-rank property of
multiway data and has been demonstrated great potential in a variety of important …

Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of
nonnegative parts-based and physically meaningful latent components from high …

Tensor provides a better representation for image space by avoiding information loss in
vectorization. Nonnegative tensor factorization (NTF), whose objective is to express an n …

Tensor ring (TR) decomposition, which factorizes a tensor into a sequence of cyclically
interconnected third-order TR factors, is a powerful tool to capture the global low-rankness of …

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from
the laborious model selection problem due to their high model sensitivity. In particular, for …

Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The
widely used alternating least squares (ALS) method, which solves a sequence of over …

Low-rank representation (LRR) can recover clean data from noisy data while effectively
characterizing the subspace structures between data, therefore, it becomes one of the state …

Multidimensional data (ie, tensors) with missing entries are common in practice. Extracting
features from incomplete tensors is an important yet challenging problem in many fields …