Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization Article Swipe
E. Hung
,
Enio Hodzic
,
Zhixin Cyrillus Tan
,
Aaron S. Meyer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.07.05.602272
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.07.05.602272
Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.
Related Topics
Concepts
Missing data
Imputation (statistics)
Computer science
Dimensionality reduction
Least-squares function approximation
Benchmark (surveying)
Data mining
Multilinear map
Factorization
Tensor (intrinsic definition)
Algorithm
Generalized least squares
Statistics
Mathematics
Artificial intelligence
Pattern recognition (psychology)
Machine learning
Geography
Geodesy
Pure mathematics
Estimator
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.07.05.602272
- OA Status
- green
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400490994
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400490994Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/2024.07.05.602272Digital Object Identifier
- Title
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Censored Least Squares for Imputing Missing Values in PARAFAC Tensor FactorizationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-10Full publication date if available
- Authors
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E. Hung, Enio Hodzic, Zhixin Cyrillus Tan, Aaron S. MeyerList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.07.05.602272Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11257416Direct OA link when available
- Concepts
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Missing data, Imputation (statistics), Computer science, Dimensionality reduction, Least-squares function approximation, Benchmark (surveying), Data mining, Multilinear map, Factorization, Tensor (intrinsic definition), Algorithm, Generalized least squares, Statistics, Mathematics, Artificial intelligence, Pattern recognition (psychology), Machine learning, Geography, Geodesy, Pure mathematics, EstimatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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