tag:joss.theoj.org,2005:/papers/tagged/Unsupervised%20learningJournal of Open Source Software2023-11-17T14:46:05ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46322023-11-17T14:46:05Z2023-11-28T02:34:05ZDREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinatesaccepted0.3.02023-07-28 17:34:30 UTC912023-11-17 14:46:05 UTC820235791JoseA.PereaNortheastern University0000-0002-6440-5096LuisScoccolaUniversity of Oxford0000-0002-4862-722XChristopherJ.TralieUrsinus College0000-0003-4206-196310.21105/joss.05791https://doi.org/10.5281/zenodo.10136542Pythonhttps://joss.theoj.org/papers/10.21105/joss.05791.pdftopological data analysis, unsupervised learning, dimensionality reductiontag:joss.theoj.org,2005:Paper/40252023-05-16T14:36:36Z2023-05-17T01:09:38ZMulti-view-AE: A Python package for multi-view autoencoder modelsacceptedv1.0.02022-11-25 14:36:25 UTC852023-05-16 14:36:36 UTC820235093AnaLawryAguilaCentre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering, University College London (UCL), London, UK0000-0003-0727-3274AlejandraJaymeEngineering Mathematics and Computing Lab (EMCL), Heidelberg, GermanyNinaMontaña-BrownCentre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering, University College London (UCL), London, UK0000-0001-5685-971XVincentHeuvelineEngineering Mathematics and Computing Lab (EMCL), Heidelberg, GermanyAndreAltmannCentre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering, University College London (UCL), London, UK0000-0002-9265-239310.21105/joss.05093https://doi.org/10.5281/zenodo.7871099Pythonhttps://joss.theoj.org/papers/10.21105/joss.05093.pdfAutoencoders, Multi-view, Unsupervised learning, Representation learning, Data generationtag:joss.theoj.org,2005:Paper/40142023-03-08T10:00:40Z2023-03-09T00:04:46ZPersistable: persistent and stable clusteringaccepted0.3.22022-11-18 23:32:58 UTC832023-03-08 10:00:40 UTC820235022LuisScoccolaNortheastern University0000-0002-4862-722XAlexanderRolleTechnical University of Munich10.21105/joss.05022https://doi.org/10.5281/zenodo.7697173Python, Cythonhttps://joss.theoj.org/papers/10.21105/joss.05022.pdfclustering, unsupervised learning, machine learning, topological data analysistag:joss.theoj.org,2005:Paper/26962022-11-25T08:02:12Z2022-11-26T00:00:47ZClusterValidityIndices.jl: Batch and Incremental Metrics for Unsupervised Learningaccepted0.1.52021-05-28 15:42:10 UTC792022-11-25 08:02:12 UTC720223527SashaPetrenkoMissouri University of Science and Technology0000-0003-2442-8901DonaldC.WunschMissouri University of Science and Technology0000-0002-9726-905110.21105/joss.03527https://doi.org/10.5281/zenodo.7332045Juliahttps://joss.theoj.org/papers/10.21105/joss.03527.pdfCVI, ICVI, Cluster Validity Indices, Cluster Validity Index, Incremental Cluster Validity Indices, Incremental Cluster Validity Index, Machine Learning, Clustering, Metrics, Streaming, Time Seriestag:joss.theoj.org,2005:Paper/16912021-10-19T07:22:28Z2021-10-20T00:03:24ZTorchGAN: A Flexible Framework for GAN Training and Evaluationacceptedv0.0.42020-05-26 23:46:08 UTC662021-10-19 07:22:28 UTC620212606AvikPalIndian Institute of Technology Kanpur0000-0002-3938-7375AniketDasIndian Institute of Technology Kanpur10.21105/joss.02606https://doi.org/10.5281/zenodo.5575758Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02606.pdfDeep Learning, Machine Learning, Generative Adversarial Networks, Unsupervised Learning, Computer Vision, Generative Modelstag:joss.theoj.org,2005:Paper/11002019-09-08T12:59:51Z2021-02-15T11:31:58ZTimeSeriesClustering: An extensible framework in Juliaacceptedv0.4.22019-07-10 21:25:16 UTC412019-09-08 12:59:51 UTC420191573HolgerTeichgraeberDepartment of Energy Resources Engineering, Stanford University0000-0002-4061-2226LucasEliasKuepperDepartment of Energy Resources Engineering, Stanford University0000-0002-1992-310XAdamR.BrandtDepartment of Energy Resources Engineering, Stanford University0000-0002-2528-147310.21105/joss.01573https://doi.org/10.5281/zenodo.3385349Juliahttps://joss.theoj.org/papers/10.21105/joss.01573.pdfunsupervised learning, representative periods, optimization, machine learning, time seriestag:joss.theoj.org,2005:Paper/11152019-08-06T01:06:41Z2021-02-15T11:31:55Zivis: dimensionality reduction in very large datasets using Siamese Networksacceptedv1.2.32019-07-18 19:04:26 UTC402019-08-06 01:06:41 UTC420191596BenjaminSzubertBering LimitedIgnatDrozdovBering Limited0000-0001-6727-468810.21105/joss.01596https://doi.org/10.5281/zenodo.3360634R, Pythonhttps://joss.theoj.org/papers/10.21105/joss.01596.pdfdimensionality reduction, unsupervised learning, neural networktag:joss.theoj.org,2005:Paper/4922018-10-27T11:05:52Z2021-02-15T11:33:23ZPyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).acceptedv0.2.02018-05-08 01:02:08 UTC302018-10-27 11:05:52 UTC32018845ValentinoConstantinouNASA Jet Propulsion Laboratory0000-0002-5279-414310.21105/joss.00845https://doi.org/10.5281/zenodo.1472519Pythonhttps://joss.theoj.org/papers/10.21105/joss.00845.pdfoutlier detection, anomaly detection, probability, nearest neighbors, unsupervised learning, machine learning, statisticstag:joss.theoj.org,2005:Paper/5552018-09-02T20:57:24Z2021-02-15T11:33:12ZUMAP: Uniform Manifold Approximation and Projectionacceptedv0.3.02018-07-19 01:13:25 UTC292018-09-02 20:57:24 UTC32018861LelandMcInnesTutte Institute for Mathematics and Computing0000-0003-2143-6834JohnHealyTutte Institute for Mathematics and ComputingNathanielSaulDepartment of Mathematics and Statistics, Washington State UniversityLukasGroßbergerErnst Strüngmann Institute for Neuroscience in cooperation with Max Planck Society, Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit10.21105/joss.00861https://doi.org/10.5281/zenodo.1407268Pythonhttps://joss.theoj.org/papers/10.21105/joss.00861.pdfmanifold learning, dimension reduction, unsupervised learningtag:joss.theoj.org,2005:Paper/1562017-03-21T00:00:00Z2021-02-15T11:34:14Zhdbscan: Hierarchical density based clusteringacceptedv0.8.82017-02-26 20:46:52 UTC112017-03-21 00:00:00 UTC22017205LelandMcInnesTutte Institute for Mathematics and Computing0000-0003-2143-6834JohnHealyTutte Institute for Mathematics and ComputingSteveAstelsShopify10.21105/joss.00205https://doi.org/10.5281/zenodo.401403Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.00205.pdfclustering, unsupervised learning, machine learning