Tensor decompositions for identifying directed graph topologies and tracking dynamic networks
Y Shen, B Baingana… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Directed networks are pervasive both in nature and engineered systems, often underlying
the complex behavior observed in biological systems, microblogs and social interactions …
the complex behavior observed in biological systems, microblogs and social interactions …
Probabilistic tensor canonical polyadic decomposition with orthogonal factors
Tensor canonical polyadic decomposition (CPD), which recovers the latent factor matrices
from multidimensional data, is an important tool in signal processing. In many applications …
from multidimensional data, is an important tool in signal processing. In many applications …
New uniqueness conditions for the canonical polyadic decomposition of third-order tensors
M Sørensen, L De Lathauwer - SIAM Journal on Matrix Analysis and …, 2015 - SIAM
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 …
tensors make it an attractive tool for signal separation. However, CPD uniqueness is not yet …
Scaling probabilistic tensor canonical polyadic decomposition to massive data
Tensor canonical polyadic decomposition (CPD) has recently emerged as a promising
mathematical tool in multidimensional data analytics. Traditionally, the alternating least …
mathematical tool in multidimensional data analytics. Traditionally, the alternating least …
A new class of block coordinate algorithms for the joint eigenvalue decomposition of complex matrices
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 …
(JEVD) of a set of noisy matrices. JEVD notably occurs in source separation problems and …
Distributed graph learning with smooth data priors
ICM Nobre, M El Gheche… - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
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 …
when the underlying graph is not given explicitly. Graph learning is generally performed …
Quantization for Distributed Processing and Learning of Structured Data
I Cunha Maia Nobre - 2022 - infoscience.epfl.ch
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 …
data, is becoming increasingly popular. It represents a large portion of the data in computer …
Complex-Valued CPD, Orthogonality Constraint, and Beyond Gaussian Noises
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 …
They cannot deal with complex-valued tensor data, which, however, frequently occurs in …
Half-quadratic alternating direction method of multipliers for robust orthogonal tensor approximation
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 …
factor matrices being columnwisely orthonormal has been well studied in recent years …
Scalable Learning Adaptive to Unknown Dynamics and Graphs
Y Shen - 2019 - search.proquest.com
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 …
include the high-dimensionality and sheer volume of feature vectors that may consist of real …