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Found 3 papers, 2 papers with code
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
To tackle these challenges, we propose a {\it CO}nstrained {\it PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes.
Multi-layer Representation Learning for Medical Concepts
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
