Directed networks are pervasive both in nature and engineered systems, often underlying
the complex behavior observed in biological systems, microblogs and social interactions …

Tensor canonical polyadic decomposition (CPD), which recovers the latent factor matrices
from multidimensional data, is an important tool in signal processing. In many applications …

The uniqueness properties of the canonical polyadic decomposition (CPD) of higher-order
tensors make it an attractive tool for signal separation. However, CPD uniqueness is not yet …

Tensor canonical polyadic decomposition (CPD) has recently emerged as a promising
mathematical tool in multidimensional data analytics. Traditionally, the alternating least …

Several signal processing problems can be written as the joint eigenvalue decomposition
(JEVD) of a set of noisy matrices. JEVD notably occurs in source separation problems and …

Graph learning is often a necessary step in processing or representing structured data,
when the underlying graph is not given explicitly. Graph learning is generally performed …

In the domains of machine learning, data science and signal processing, graph or network
data, is becoming increasingly popular. It represents a large portion of the data in computer …

Abstract In previous chapters, Bayesian CPDs are developed for real-valued tensor data.
They cannot deal with complex-valued tensor data, which, however, frequently occurs in …

Higher-order tensor canonical polyadic decomposition (CPD) with one or more of the latent
factor matrices being columnwisely orthonormal has been well studied in recent years …

With the scale of information growing every day, the key challenges in machine learning
include the high-dimensionality and sheer volume of feature vectors that may consist of real …