Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306789069> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4306789069 endingPage "103109" @default.
- W4306789069 startingPage "103109" @default.
- W4306789069 abstract "With an increase in the number of data instances, data processing operations (e.g. clustering) requires an increasing amount of computational resources, and it is often the case that for considerably large datasets such operations cannot be executed on a single workstation. This requires the use of a server computer for carrying out the operations. However, to ensure privacy of the shared data, a privacy preserving data processing workflow involves applying an encoding transformation on the set of data points prior to applying the computation. This encoding should ideally cater to two objectives—first, it should be difficult to reconstruct the data, second, the results of the operation executed on the encoded space should be as close as possible to the results of the same operation executed on the original data. While standard encoding mechanisms, such as locality sensitive hashing, caters to the first objective, the second objective may not always be adequately satisfied. In this paper, we specifically focus on ‘clustering’ as the data processing operation. We apply a deep metric learning approach to learn a parameterized encoding transformation function with an objective to maximize the alignment of the clusters in the encoded space to those in the original data. We conduct experimentation on four standard benchmark datasets, particularly MNIST, Fashion-MNIST (each dataset contains 70K grayscale images), CIFAR-10 consisting of 60K color images and 20-Newsgroups containing 18K news articles. Our experiments demonstrate that the proposed method yields better clusters in comparison to approaches where the encoding process is agnostic of the clustering objective. • Proposed an encoding-based workflow of data clustering that preserves data privacy. • Proposed solution is suitable for deployment in a distributed computing environment. • A weakly supervised approach is used to learn a parameterized similarity function. • Effective reconstruction of data is achived with additional statistical information. • Experiments on image and text datasets shows the efficacy of the proposed method." @default.
- W4306789069 created "2022-10-20" @default.
- W4306789069 creator A5000936680 @default.
- W4306789069 creator A5037394429 @default.
- W4306789069 creator A5082339849 @default.
- W4306789069 creator A5090127362 @default.
- W4306789069 date "2023-01-01" @default.
- W4306789069 modified "2023-10-11" @default.
- W4306789069 title "Weakly supervised deep metric learning on discrete metric spaces for privacy-preserved clustering" @default.
- W4306789069 cites W19235576 @default.
- W4306789069 cites W2009733253 @default.
- W4306789069 cites W2012453466 @default.
- W4306789069 cites W2023473991 @default.
- W4306789069 cites W2038276547 @default.
- W4306789069 cites W2080937027 @default.
- W4306789069 cites W2106463421 @default.
- W4306789069 cites W2112796928 @default.
- W4306789069 cites W2124612670 @default.
- W4306789069 cites W2147717514 @default.
- W4306789069 cites W2190633754 @default.
- W4306789069 cites W2536015822 @default.
- W4306789069 cites W2605170010 @default.
- W4306789069 cites W2608862709 @default.
- W4306789069 cites W2762887147 @default.
- W4306789069 cites W2767849480 @default.
- W4306789069 cites W2889507104 @default.
- W4306789069 cites W2895347732 @default.
- W4306789069 cites W2896037409 @default.
- W4306789069 cites W2917666053 @default.
- W4306789069 cites W2948077755 @default.
- W4306789069 cites W2953271441 @default.
- W4306789069 cites W2981052417 @default.
- W4306789069 cites W2989429815 @default.
- W4306789069 cites W3034607353 @default.
- W4306789069 cites W3035204982 @default.
- W4306789069 cites W3043465744 @default.
- W4306789069 cites W3085052658 @default.
- W4306789069 cites W3088229056 @default.
- W4306789069 cites W3094463742 @default.
- W4306789069 cites W3099206234 @default.
- W4306789069 cites W3107670916 @default.
- W4306789069 cites W3127245836 @default.
- W4306789069 cites W3135955057 @default.
- W4306789069 cites W3197665185 @default.
- W4306789069 doi "https://doi.org/10.1016/j.ipm.2022.103109" @default.
- W4306789069 hasPublicationYear "2023" @default.
- W4306789069 type Work @default.
- W4306789069 citedByCount "2" @default.
- W4306789069 countsByYear W43067890692023 @default.
- W4306789069 crossrefType "journal-article" @default.
- W4306789069 hasAuthorship W4306789069A5000936680 @default.
- W4306789069 hasAuthorship W4306789069A5037394429 @default.
- W4306789069 hasAuthorship W4306789069A5082339849 @default.
- W4306789069 hasAuthorship W4306789069A5090127362 @default.
- W4306789069 hasConcept C118615104 @default.
- W4306789069 hasConcept C144133560 @default.
- W4306789069 hasConcept C154945302 @default.
- W4306789069 hasConcept C162853370 @default.
- W4306789069 hasConcept C176217482 @default.
- W4306789069 hasConcept C198043062 @default.
- W4306789069 hasConcept C23123220 @default.
- W4306789069 hasConcept C33923547 @default.
- W4306789069 hasConcept C41008148 @default.
- W4306789069 hasConcept C73555534 @default.
- W4306789069 hasConceptScore W4306789069C118615104 @default.
- W4306789069 hasConceptScore W4306789069C144133560 @default.
- W4306789069 hasConceptScore W4306789069C154945302 @default.
- W4306789069 hasConceptScore W4306789069C162853370 @default.
- W4306789069 hasConceptScore W4306789069C176217482 @default.
- W4306789069 hasConceptScore W4306789069C198043062 @default.
- W4306789069 hasConceptScore W4306789069C23123220 @default.
- W4306789069 hasConceptScore W4306789069C33923547 @default.
- W4306789069 hasConceptScore W4306789069C41008148 @default.
- W4306789069 hasConceptScore W4306789069C73555534 @default.
- W4306789069 hasIssue "1" @default.
- W4306789069 hasLocation W43067890691 @default.
- W4306789069 hasOpenAccess W4306789069 @default.
- W4306789069 hasPrimaryLocation W43067890691 @default.
- W4306789069 hasRelatedWork W1534720161 @default.
- W4306789069 hasRelatedWork W2083665254 @default.
- W4306789069 hasRelatedWork W2132641928 @default.
- W4306789069 hasRelatedWork W2393816671 @default.
- W4306789069 hasRelatedWork W2804364458 @default.
- W4306789069 hasRelatedWork W2804957450 @default.
- W4306789069 hasRelatedWork W2942177010 @default.
- W4306789069 hasRelatedWork W4297851895 @default.
- W4306789069 hasRelatedWork W4298130764 @default.
- W4306789069 hasRelatedWork W4310225030 @default.
- W4306789069 hasVolume "60" @default.
- W4306789069 isParatext "false" @default.
- W4306789069 isRetracted "false" @default.
- W4306789069 workType "article" @default.