Search Results for author:
Found 8 papers, 5 papers with code
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
However, from a practical perspective, the choice of regularizers and regularization coefficients, as well as the design of efficient algorithms, is challenging because of the multifactor nature of these models and the lack of theory to back these choices.
Dictionary-based Low-Rank Approximations and the Mixed Sparse Coding problem
Constrained tensor and matrix factorization models allow to extract interpretable patterns from multiway data.
An AO-ADMM approach to constraining PARAFAC2 on all modes
We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
PARAFAC2 AO-ADMM: Constraints in all modes
The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/PARAFAC (CP) model for tensor decompositions.
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
Coupled matrix and tensor factorizations (CMTF) are frequently used to jointly analyze data from multiple sources, also called data fusion.
Sparse Separable Nonnegative Matrix Factorization
We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions.
Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF).
Nonnegative PARAFAC2: a flexible coupling approach
In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode.
