2021
DOI: 10.21203/rs.3.pex-1636/v1
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Abstract: We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can improve generalization by preventing latent variables to overfit data used to conduct the dimensional reduction. However, this technique may warrant more computational ca… Show more

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Cited by 5 publications
(9 citation statements)
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“…They were latent variables that represented the 37 predictors but with different weights. Details are described elsewhere on how the weights were inferred [ 62 ]. We visualized the absolute values of these weights for each selected PC ( Fig 3 ).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…They were latent variables that represented the 37 predictors but with different weights. Details are described elsewhere on how the weights were inferred [ 62 ]. We visualized the absolute values of these weights for each selected PC ( Fig 3 ).…”
Section: Resultsmentioning
confidence: 99%
“…We also used average values of data partitioned for model development to get those PCs for model validation. This study’s resampled dimensional reduction method was already described elsewhere [ 62 ].…”
Section: Methodsmentioning
confidence: 99%
“…A surrogate transcriptome model was only developed for a gene with greater than or equal to three instances for the minority outcome and a minimum of two candidate predictors. Considering the tradeoff between the number of genes fulfilling the aforementioned criteria and the risk of bias due to a small sample size, we applied a protocol to reduce the number of candidate predictors without leaking the outcome information to prevent overfitting, as described previously [31]. This resulted in cross-validated principal components (PCs) which were used as candidate predictors.…”
Section: Derivation Of the Maternal-fetal Interface Transcriptome Fro...mentioning
confidence: 99%
“…We proposed an analysis pipeline using several protocols previously described elsewhere. [9][10][11][12] The rst building block of this pipeline is a systematic human learning algorithm. 9 Based on hypotheticodeductive reasoning, human learning involves collection of prior knowledge to construct a causal diagram as a central assumption for hypothesis testing.…”
Section: Introductionmentioning
confidence: 99%
“…We proposed a resampling protocol for dimensional reduction resulting a few latent variables. 11 Most prediction models that used latent variables, if not all, conducted a dimensional reduction without either resampling or data partition, which exposed to a risk of optimistic bias, and is not robust for samples beyond the training set. This is because resampling or data partition are more well-known in either predictive modeling or supervised machine learning, compared to a dimensional reduction that is typically used for statistical inference and unsupervised machine learning.…”
Section: Introductionmentioning
confidence: 99%