Differentially Private Singular Value Decomposition for Training Support Vector Machines.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
25
01
2022
revised:
16
02
2022
accepted:
21
02
2022
entrez:
5
4
2022
pubmed:
6
4
2022
medline:
7
4
2022
Statut:
epublish
Résumé
Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Principal component analysis (PCA) can project instances into a low-dimensional subspace while capturing the variance of the matrix
Identifiants
pubmed: 35378802
doi: 10.1155/2022/2935975
pmc: PMC8976603
doi:
Types de publication
Journal Article
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
2935975Commentaires et corrections
Type : RetractionIn
Informations de copyright
Copyright © 2022 Zhenlong Sun et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
Références
J Mach Learn Res. 2011 Mar;12:1069-1109
pubmed: 21892342
Biomed Res Int. 2014;2014:827371
pubmed: 25013805
IEEE Trans Neural Netw Learn Syst. 2021 Apr 29;PP:
pubmed: 33914687
PeerJ Comput Sci. 2021 Dec 1;7:e799
pubmed: 34977353