Pith. Sign in

RECORD

Multiple testing for outlier detection in functional data

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Clémentine Barreyre, Béatrice Laurent (IMT), Jean-Michel Loubes (IMT), Bertrand Cabon, Loïc Boussouf

Machine Learning · Submitted 13 Dec 2017

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

The authors' main claim This procedure to select the features is applied on simulated data that mimic the behaviour of space telemetries, and compared with existing dimension reduction techniques.

how it is stated · asserted 0.99 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1712.04775 (version 1)
Pith ID4TH2RELX, Pith's permanent record of this paper and its review
CitationB\'eatrice Laurent (IMT), Bertrand Cabon, Cl\'ementine Barreyre, Jean-Michel Loubes (IMT), Lo\"ic Boussouf. "Multiple testing for outlier detection in functional data." arXiv:1712.04775 (2017). https://pith.science/paper/4TH2RELX
Claim paper

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.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,227 input tokens.

What the abstract itself states, as probabilities: claims first 0.16 · states limitation 0.06 · human participants 0.03 · controlled comparison 0.37 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.93 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

We propose a novel procedure for outlier detection in functional data, in a semi-supervised framework. As the data is functional, we consider the coefficients obtained after projecting the observations onto orthonormal bases (wavelet, PCA). A multiple testing procedure based on the two-sample test is defined in order to highlight the levels of the coefficients on which the outliers appear as significantly different to the normal data. The selected coefficients are then called features for the outlier detection, on which we compute the Local Outlier Factor to highlight the outliers. This procedure to select the features is applied on simulated data that mimic the behaviour of space telemetries, and compared with existing dimension reduction techniques.

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 novel procedure for outlier detection in functional data, in a semi-supervised framework. contribution 1.00 0.91 0.14 asserted 0.99
s1 As the data is functional, we consider the coefficients obtained after projecting the observations onto orthonormal bases (wavelet, PCA). method 0.93 0.70 0.02 asserted 0.99
s2 A multiple testing procedure based on the two-sample test is defined in order to highlight the levels of the coefficients on which the outliers appear as significantly different to the normal data. method 0.98 0.88 0.07 asserted 1.00
s3 The selected coefficients are then called features for the outlier detection, on which we compute the Local Outlier Factor to highlight the outliers. method 0.99 0.89 0.10 asserted 1.00
s4 This procedure to select the features is applied on simulated data that mimic the behaviour of space telemetries, and compared with existing dimension reduction techniques. method 0.76 0.92 0.12 asserted 0.99

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

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • 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 novel procedure for outlier detection in functional data, in a semi-supervised framework. As the data is functional, we consider the coefficients obtained after projecting the observations onto orthonormal bases (wavelet, PCA). A multiple testing procedure based on the two-sample test is defined in order to highlight the levels of the coefficients on which the outliers appear as significantly different to the normal data. The selected coefficients are then called features for the outlier detection, on which we compute the Local Outlier Factor to highlight the outliers. This procedure to select the features is applied on simulated data that mimic the behaviour of space telemetries, and compared with existing dimension reduction techniques.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.