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Found 7 papers, 1 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.
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
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.
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
It can also do so more accurately and in less time than the state-of-the-art: on a 12th order subset of the input data, Sparse H-Tucker is 18x more accurate and 7. 5x faster than a previously state-of-the-art method.
Guaranteed Scalable Learning of Latent Tree Models
We present an integrated approach for structure and parameter estimation in latent tree graphical models.
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method.
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor.
