2020 25th International Conference on Pattern Recognition (ICPR) 2021
DOI: 10.1109/icpr48806.2021.9413180
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Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP

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Cited by 5 publications
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“…Uniform manifold approximation and projection (UMAP) is a nonlinear dimensionality reduction technique based on manifold learning [54], which is widely used for visualization, exploratory data analysis, and clustering and classification tasks [55,56]. In addition to UMAP, there are many dimensionality reduction algorithms, such as the principal component analysis (PCA), multidimensional scaling, Sammon's mapping, and T-distributed random neighbor embedding (t-SNE), etc.…”
Section: Umapmentioning
confidence: 99%
See 1 more Smart Citation
“…Uniform manifold approximation and projection (UMAP) is a nonlinear dimensionality reduction technique based on manifold learning [54], which is widely used for visualization, exploratory data analysis, and clustering and classification tasks [55,56]. In addition to UMAP, there are many dimensionality reduction algorithms, such as the principal component analysis (PCA), multidimensional scaling, Sammon's mapping, and T-distributed random neighbor embedding (t-SNE), etc.…”
Section: Umapmentioning
confidence: 99%
“…In addition to UMAP, there are many dimensionality reduction algorithms, such as the principal component analysis (PCA), multidimensional scaling, Sammon's mapping, and T-distributed random neighbor embedding (t-SNE), etc. The performance of the UMAP algorithm significantly outranks other non-linear dimensionality reduction methods [56]. Compared with PCA, UMAP can precisely capture the nonlinear structure of large data sets [57].…”
Section: Umapmentioning
confidence: 99%