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Found 7 papers, 1 papers with code
Least-squares methods for nonnegative matrix factorization over rational functions
When the data is made of samplings of continuous signals, the factors in NMF can be constrained to be samples of nonnegative rational functions, which allow fairly general models; this is referred to as NMF using rational functions (R-NMF).
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective
The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization.
Early soft and flexible fusion of EEG and fMRI via tensor decompositions
The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets.
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation
We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formulated.
Tensor Decomposition for Signal Processing and Machine Learning
Tensors or {\em multi-way arrays} are functions of three or more indices $(i, j, k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r, c)$ for (row, column).
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties
We present a general framework to learn functions in tensor product reproducing kernel Hilbert spaces (TP-RKHSs).
