Zobrazeno 1 - 10
of 249
pro vyhledávání: '"Brie David"'
Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often het
Externí odkaz:
http://arxiv.org/abs/2412.01102
This article characterizes the rank-one factorization of auto-correlation matrix polynomials. We establish a sufficient and necessary uniqueness condition for uniqueness of the factorization based on the greatest common divisor (GCD) of multiple poly
Externí odkaz:
http://arxiv.org/abs/2308.15106
Autor:
Borsoi, Ricardo Augusto, Lehmann, Isabell, Akhonda, Mohammad Abu Baker Siddique, Calhoun, Vince, Usevich, Konstantin, Brie, David, Adali, Tülay
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data. Coupled matri
Externí odkaz:
http://arxiv.org/abs/2211.14253
This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval problems. We provide a complete characterization of uniqueness proper
Externí odkaz:
http://arxiv.org/abs/2206.12868
Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN. In this work, we are particularly interested in the estimation of flexible activation funct
Externí odkaz:
http://arxiv.org/abs/2106.13542
This paper introduces a general framework for solving constrained convex quaternion optimization problems in the quaternion domain. To soundly derive these new results, the proposed approach leverages the recently developed generalized $\mathbb{HR}$-
Externí odkaz:
http://arxiv.org/abs/2102.02763
Autor:
Borsoi, Ricardo Augusto, Prévost, Clémence, Usevich, Konstantin, Brie, David, Bermudez, José Carlos Moreira, Richard, Cédric
Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previous
Externí odkaz:
http://arxiv.org/abs/2006.16968
This article introduces quaternion non-negative matrix factorization (QNMF), which generalizes the usual non-negative matrix factorization (NMF) to the case of polarized signals. Polarization information is represented by Stokes parameters, a set of
Externí odkaz:
http://arxiv.org/abs/1903.10593
We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupl
Externí odkaz:
http://arxiv.org/abs/1811.11091
Publikováno v:
In Signal Processing September 2022 198
