Results 1 to 10 of about 9,586,270 (207)
Spatially aware dimension reduction for spatial transcriptomics [PDF]
Nature Communications, 2022 Spatial transcriptomics analyses can be affected by noise and spatial correlation across tissue locations. Here, the authors develop SpatialPCA, a spatially-aware dimensionality reduction method that explicitly models spatial correlation structures, and ...
Lulu Shang, Xiang Zhou
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Evolutionary dimension reduction in phenotypic space [PDF]
Physical Review Research, 2020 In general, cellular phenotypes, as measured by concentrations of cellular components, involve large number of degrees of freedom. However, recent measurement has demonstrated that phenotypic changes resulting from adaptation and evolution in response to
Takuya U. Sato, Kunihiko Kaneko
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Dimension Reduction for Fréchet Regression. [PDF]
J Am Stat Assoc, 2023 With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Peterson & Müller 2019) provides a promising framework for regression analysis with metric space-valued responses.
Zhang Q, Xue L, Li B.
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Sufficient Dimension Reduction: An Information-Theoretic Viewpoint [PDF]
Entropy, 2022 There has been a lot of interest in sufficient dimension reduction (SDR) methodologies, as well as nonlinear extensions in the statistics literature. The SDR methodology has previously been motivated by several considerations: (a) finding data-driven ...
Debashis Ghosh
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Spectral Dimension Reduction of Complex Dynamical Networks [PDF]
Physical Review X, 2019 Dynamical networks are powerful tools for modeling a broad range of complex systems, including financial markets, brains, and ecosystems. They encode how the basic elements (nodes) of these systems interact altogether (via links) and evolve (nodes ...
Edward Laurence +3 more
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Dimension and Dimensional Reduction in Quantum Gravity
Universe, 2019 If gravity is asymptotically safe, operators will exhibit anomalous scaling at the ultraviolet fixed point in a way that makes the theory effectively two-dimensional.
Steven Carlip
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Dimension reduction methods for microarray data: a review
AIMS Bioengineering, 2017 Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data.
Rabia Aziz +2 more
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Dimension Reduction for Efficient Data-Enabled Predictive Control [PDF]
IEEE Control Systems Letters, 2022 In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC).
Kaixiang Zhang, Yang Zheng, Zhaojian Li
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Communications Biology, 2022 Dimension reduction (DR) algorithms project data from high dimensions to lower dimensions to enable visualization of interesting high-dimensional structure. DR algorithms are widely used for analysis of single-cell transcriptomic data. Despite widespread
Haiyang Huang +3 more
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bioRxiv, 2021 Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step.
Wei Liu +7 more
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