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LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.

, , , , , and . KDD, page 1625-1635. ACM, (2020)

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CP-ORTHO: An Orthogonal Tensor Factorization Framework for Spatio-Temporal Data., , , , , , and . SIGSPATIAL/GIS, page 67:1-67:4. ACM, (2017)SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors., , , , , , and . AAAI, page 6548-6556. AAAI Press, (2021)LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values., , , , , and . KDD, page 1625-1635. ACM, (2020)Imputation of missing values for electronic health record laboratory data., , , , , , , , , and 2 other author(s). npj Digit. Medicine, (2021)SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors., , , , , , and . CoRR, (2020)Team formation in social networks based on local distance metric., , , and . FSKD, page 946-952. IEEE, (2015)A parameter-free method for detecting local communities based on attainable information., , and . ASONAM, page 354-357. IEEE Computer Society, (2016)TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records., , , , , , , and . CoRR, (2019)TASTE: temporal and static tensor factorization for phenotyping electronic health records., , , , , , , and . CHIL, page 193-203. ACM, (2020)COPA: Constrained PARAFAC2 for Sparse & Large Datasets., , , , , and . CIKM, page 793-802. ACM, (2018)