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Found 9 papers, 1 papers with code
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
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.
Polarimetric phase retrieval: uniqueness and algorithms
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.
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels.
Tensor-based framework for training flexible neural networks
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.
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model.
Structured low-rank matrix completion for forecasting in time series analysis
In this paper we consider the low-rank matrix completion problem with specific application to forecasting in time series analysis.
Identifiability of an X-rank decomposition of polynomial maps
In this paper, we study a polynomial decomposition model that arises in problems of system identification, signal processing and machine learning.
Adjusted least squares fitting of algebraic hypersurfaces
In this paper, we present new results on invariance properties of the adjusted least squares estimator and an improved algorithm for computing the estimator for an arbitrary set of monomials in the polynomial equation.
