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Found 11 papers, 6 papers with code
Scalable Tensor Factorizations for Incomplete Data
In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries.
Numerical Analysis Numerical Analysis Data Analysis, Statistics and Probability G.1.3; G.1.6
Temporal Link Prediction using Matrix and Tensor Factorizations
We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition.
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity.
Cross-product Penalized Component Analysis (XCAN)
In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation.
Tracing Network Evolution Using the PARAFAC2 Model
For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.
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.
PARAFAC2 AO-ADMM: Constraints in all modes
The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/PARAFAC (CP) model for tensor decompositions.
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.
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Low-rank data approximation methods such as matrix (e. g., non-negative matrix factorization) and tensor decompositions (e. g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights.
PARAFAC2-based Coupled Matrix and Tensor Factorizations
Coupled matrix and tensor factorizations (CMTF) have emerged as an effective data fusion tool to jointly analyze data sets in the form of matrices and higher-order tensors.
A Time-aware tensor decomposition for tracking evolving patterns
Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode.
