{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T18:04:42Z","timestamp":1758823482944,"version":"3.38.0"},"reference-count":83,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Information Visualization"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p> Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our \u201cHigh-Dimensional Sharpened DR\u201d (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog. <\/jats:p>","DOI":"10.1177\/14738716221086589","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T07:21:33Z","timestamp":1650525693000},"page":"197-219","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Visual cluster separation using high-dimensional sharpened dimensionality reduction"],"prefix":"10.1177","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-163X","authenticated-orcid":false,"given":"Youngjoo","family":"Kim","sequence":"first","affiliation":[{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands"}]},{"given":"Alexandru C","family":"Telea","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"}]},{"given":"Scott C","family":"Trager","sequence":"additional","affiliation":[{"name":"Kapteyn Astronomical Institute, University of Groningen, Groningen, The Netherlands"}]},{"given":"Jos","family":"BTM Roerdink","sequence":"additional","affiliation":[{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands"}]}],"member":"179","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"key":"bibr1-14738716221086589","first-page":"2579","volume":"9","author":"van der Maaten L","year":"2009","journal-title":"J Mach Learn Res"},{"key":"bibr2-14738716221086589","doi-asserted-by":"publisher","DOI":"10.1051\/0004-6361\/201833099"},{"first-page":"143","volume-title":"Proceedings of the sixteenth conference on uncertainty in artificial intelligence","author":"Dasgupta S","key":"bibr3-14738716221086589"},{"volume-title":"arXiv preprint","year":"2017","author":"Xie 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