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Primal path algorithm for compositional data analysis

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Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi

Machine Learning · Submitted 21 Dec 2018

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

The authors' main claim As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data.

how it is stated · asserted 0.99 read the record →

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arXiv ID1812.08954 (version 1)
Pith IDV2X2HOTH, Pith's permanent record of this paper and its review
CitationHosik Choi, Jong-June Jeon, Sungho Won, Yongdai Kim. "Primal path algorithm for compositional data analysis." arXiv:1812.08954 (2018). https://pith.science/paper/V2X2HOTH
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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.12 · human participants 0.47 · controlled comparison 0.10 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.95 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; .

Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. background 1.00 0.06 0.03 asserted 1.00
s1 To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. background 0.99 0.05 0.03 asserted 0.99
s2 However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. background 0.51 0.11 0.03 asserted 1.00
s3 As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. contribution 0.95 0.93 0.34 asserted 0.99
s4 The algorithm is then extended to a classification model with compositional predictors. contribution 0.84 0.92 0.08 asserted 1.00
s5 We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data. contribution 0.77 0.95 0.16 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.55.

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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

The paper
Original abstract

Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data.

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