Results 11 to 20 of about 325,444 (301)
CoRR, 2021 83 pages, 36 figures, to appear in the Cambridge University Press Elements in Quantitative and Computational Methods for the Social Sciences ...
Waggoner, Philip D.
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, 2021 When data objects that are the subject of analysis using machine learning techniques are described by a large number of features (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data. Dimension reduction can be beneficial not only for reasons of computational efficiency but also because it can improve the accuracy
Cunningham, Pádraig
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LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
Journal of Statistical Software, 2011 We introduce a new mlab software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation ...
R. Dennis Cook +2 more
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Hyperspectral Image Classification via Information Theoretic Dimension Reduction
Remote Sensing, 2023 Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals.
Md Rashedul Islam +4 more
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AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION [PDF]
, 2014 Sufficient dimension reduction [Li 1991] has long been a prominent issue in multivariate nonparametric regression analysis. To uncover the central dimension reduction space, we propose in this paper an adaptive composite quantile approach.
Kong, Efang
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Evolutionary dimension reduction in phenotypic space
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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kag85/RHEED-Dimension-Reduction: Journal of Applied Physics
, 2021 An explanation of how to use dimension reduction methods PCA, NMF, and kmeans, on RHEED ...
kag85
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Quantile treatment effect estimation with dimension reduction
Statistical Theory and Related Fields, 2020 Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.
Ying Zhang +3 more
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Dimension reduction with expectation of conditional difference measure
Statistical Theory and Related Fields, 2023 In this article, we introduce a flexible model-free approach to sufficient dimension reduction analysis using the expectation of conditional difference measure.
Wenhui Sheng, Qingcong Yuan
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A Nonlinear Approach to Dimension Reduction
Discrete & Computational Geometry, 2011 The $l_2$ flattening lemma of Johnson and Lindenstrauss [JL84] is a powerful tool for dimension reduction. It has been conjectured that the target dimension bounds can be refined and bounded in terms of the intrinsic dimensionality of the data set (for example, the doubling dimension).
Lee-Ad Gottlieb, Robert Krauthgamer
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