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Finding Alternate Features in Lasso

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Satoshi Hara, Takanori Maehara

Machine Learning · Submitted 18 Nov 2016

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

The authors' main claim We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features.

how it is stated · asserted 1.00 read the record →

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arXiv ID1611.05940 (version 2)
Pith ID5EXA4D23, Pith's permanent record of this paper and its review
CitationSatoshi Hara, Takanori Maehara. "Finding Alternate Features in Lasso." arXiv:1611.05940 (2016). https://pith.science/paper/5EXA4D23
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,743 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.16 · human participants 0.04 · controlled comparison 0.10 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.35 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We propose a method for finding alternate features missing in the Lasso optimal solution. In ordinary Lasso problem, one global optimum is obtained and the resulting features are interpreted as task-relevant features. However, this can overlook possibly relevant features not selected by the Lasso. With the proposed method, we can provide not only the Lasso optimal solution but also possible alternate features to the Lasso solution. We show that such alternate features can be computed efficiently by avoiding redundant computations. We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We propose a method for finding alternate features missing in the Lasso optimal solution. contribution 1.00 0.89 0.26 asserted 1.00
s1 In ordinary Lasso problem, one global optimum is obtained and the resulting features are interpreted as task-relevant features. background 1.00 0.08 0.04 asserted 1.00
s2 However, this can overlook possibly relevant features not selected by the Lasso. gap 0.66 0.09 0.05 hedged possibility 0.86
s3 With the proposed method, we can provide not only the Lasso optimal solution but also possible alternate features to the Lasso solution. contribution 0.96 0.86 0.50 asserted 0.71
s4 We show that such alternate features can be computed efficiently by avoiding redundant computations. contribution 0.41 0.93 0.15 asserted 1.00
s5 We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features. result 0.93 0.95 0.29 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.60.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

We propose a method for finding alternate features missing in the Lasso optimal solution. In ordinary Lasso problem, one global optimum is obtained and the resulting features are interpreted as task-relevant features. However, this can overlook possibly relevant features not selected by the Lasso. With the proposed method, we can provide not only the Lasso optimal solution but also possible alternate features to the Lasso solution. We show that such alternate features can be computed efficiently by avoiding redundant computations. We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features.

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2 arXiv papers cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Uses its method or tool 1 paper
Cites it as background 1 paper
  • The approaches to interpretable models go in several distinct directions – producing sparse models [ref] , visualization techniques [ref] , hybrid models [ref] , input data segmentation [ref] , and model diagnostics with or without blackbox interpretation layers [ref] to name a few prominent directions.

    Fibres of Failure: Classifying errors in predictive processes · Introduction · 0.99

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