Pith. Sign in

RECORD

A survey of manifold learning and its applications for multimedia

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
Hannes Fassold

Multimedia · Submitted 8 Sep 2023

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

The authors' main claim In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia.

how it is stated · asserted 0.84 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2310.12986 (version 1)
Pith IDIFCRDNFV, Pith's permanent record of this paper and its review
CitationHannes Fassold. "A survey of manifold learning and its applications for multimedia." arXiv:2310.12986 (2023). https://pith.science/paper/IFCRDNFV
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, 2,188 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.04 · human participants 0.05 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.02 · compares with prior results 0.03 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Manifold learning is an emerging research domain of machine learning. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia.

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 Manifold learning is an emerging research domain of machine learning. background 1.00 0.05 0.03 asserted 1.00
s1 In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia. contribution 1.00 0.78 0.11 asserted 0.84

What kind of paper the classifier reports this is: review 1.00 · evidence from literature 0.96.

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

Manifold learning is an emerging research domain of machine learning. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia.

Human review

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

How the literature uses it

1 arXiv paper cite this in 1 sentence

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

Cites it as background 1 paper
  • Manifold learning has been useful in many fields such as image classification and object detection [ref] , image synthesis and enhancement [ref] , video analysis [ref] , 3D data processing [ref] , analyzing single-cell RNA-sequencing data [ref] , and more [ref]

    Noisy Data Visualization using Functional Data Analysis · Introduction · 0.86

Discussion

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