Computational Information Geometry for Binary Classification of High-Dimensional Random Tensors.

Gia-Thuy Pham, Rémy Boyer, Frank Nielsen

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An Elementary Introduction to Information Geometry.Entropy (Basel, Switzerland) · 2020
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gia-Thuy PhamLaboratory of Signals and Systems (L2S), Department of Signals and Statistics, University of Paris-Sud, 91400 Orsay, France.

Rémy BoyerLaboratory of Signals and Systems (L2S), Department of Signals and Statistics, University of Paris-Sud, 91400 Orsay, France.

Frank NielsenComputer Science Department LIX, École Polytechnique, 91120 Palaiseau, France.ORCID 0000-0001-5728-0726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating the performance of Bayesian classification in a high-dimensional random tensor is a fundamental problem, usually difficult and under-studied. In this work, we consider two Signal to Noise Ratio (SNR)-based binary classification problems of interest. Under the alternative hypothesis, i.e., for a non-zero SNR, the observed signals are either a noisy rank-

Indexed as

Chernoff/Bhattacharyya upper boundFisher informationinformation geometrylarge random sensing matrixlarge random tensorminimal error probabilityoptimal Bayesian detection

Identifiers

PMID33265294

DOI10.3390/e20030203

PMCPMC7512719

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.