Results 31 to 40 of about 364,885 (315)
mSystems, 2021 Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial ...
George Armstrong +6 more
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Spontaneous dimensional reduction? [PDF]
AIP Conference Proceedings, 2012 To appear in Proc.
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Neighbors-Based Graph Construction for Dimensionality Reduction
IEEE Access, 2019 Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important.
Hui Tian +3 more
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Equivalence of dimensional reduction and dimensional regularisation [PDF]
Zeitschrift für Physik C Particles and Fields, 1994 For some years there has been uncertainty over whether regularisation by dimensional reduction (DRED) is viable for non-supersymmetric theories. We resolve this issue by showing that DRED is entirely equivalent to standard dimensional regularisation (DREG), to all orders in perturbation theory and for a general renormalisable theory.
I. Jack, D.R.T. Jones, K. Roberts
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Genome Biology, 2019 Background Dimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream ...
Shiquan Sun +3 more
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Cortical spatio-temporal dimensionality reduction for visual grouping [PDF]
, 2014 The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing.
Barbieri, Davide +3 more
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Proximities in dimensionality reduction
, 2022 Dimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold). Dimensionality reduction can be used for exploratory data visualization, data compression, or as a preprocessing to some other analysis in order to alleviate the curse of ...
Lee, John Aldo +3 more
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Factorization and regularization by dimensional reduction [PDF]
Physics Letters B, 2005 Since an old observation by Beenakker et al, the evaluation of QCD processes in dimensional reduction has repeatedly led to terms that seem to violate the QCD factorization theorem. We reconsider the example of the process gg->ttbar and show that the factorization problem can be completely resolved.
Signer, A., Stöckinger, D.
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Binary Whale Optimization Algorithm for Dimensionality Reduction
Mathematics, 2020 Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good ...
Abdelazim G. Hussien +4 more
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Improving Dimensionality Reduction Projections for Data Visualization
Applied Sciences, 2023 In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian +2 more
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