Smooth and probabilistic PARAFAC model with auxiliary covariates

Leying Guan

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Smooth and probabilistic PARAFAC model with auxiliary covariates

Leying Guan

Methodology · Submitted 12 Apr 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We provide intensive simulations to test different aspects of SPACO and demonstrate its use on an immunological data set from patients with SARs-CoV-2 infection.

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What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.94 · controlled comparison 0.04 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.10 · mentions code or data availability 0.02.

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arXiv listing abstract v3. Results and the main claim are underlined; show every role.

In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding. These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time dimension. We propose a smoothed probabilistic PARAFAC model with covariates (SPACO) to tackle these two problems while utilizing auxiliary covariates of interest. We provide intensive simulations to test different aspects of SPACO and demonstrate its use on an immunological data set from patients with SARs-CoV-2 infection.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular.	background 1.00	0.04	0.02	asserted 1.00
s1	Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding.	background 1.00	0.05	0.03	asserted 0.93
s2	These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time dimension.	background 0.86	0.05	0.02	asserted 0.98
s3	We propose a smoothed probabilistic PARAFAC model with covariates (SPACO) to tackle these two problems while utilizing auxiliary covariates of interest.	contribution 0.67	0.93	0.24	asserted 0.99
s4	We provide intensive simulations to test different aspects of SPACO and demonstrate its use on an immunological data set from patients with SARs-CoV-2 infection.	contribution 0.79	0.96	0.20	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.95.

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arXiv listing abstract v3

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding. These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time dimension. We propose a smoothed probabilistic PARAFAC model with covariates (SPACO) to tackle these two problems while utilizing auxiliary covariates of interest. We provide intensive simulations to test different aspects of SPACO and demonstrate its use on an immunological data set from patients with SARs-CoV-2 infection.

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Cites as background 1 paper

Tensor decomposition has been applied to many applications, including speech signal separation [ref] , topic modeling [ref] , communication [ref] , spectral hypergraph theory and hyper-graph matching [ref] , and analysis of biomedical multi-omics data [ref] .

Tensor decompositions for learning latent variable models · Introduction · 0.87

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