Results 11 to 20 of about 294,873 (258)
Multi-Label Learning via Feature and Label Space Dimension Reduction
IEEE Access, 2020 In multi-label learning, each object belongs to multiple class labels simultaneously. In the data explosion age, the size of data is often huge, i.e., large number of instances, features and class labels.
Jun Huang +4 more
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CoRR, 2021 83 pages, 36 figures, to appear in the Cambridge University Press Elements in Quantitative and Computational Methods for the Social Sciences ...
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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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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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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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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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Application of Dimension Reduction Methods for Stress Detection
International Journal of Pioneering Technology and Engineering, 2023 Effective detection of stress situations plays an important role in combating it. This is the main source of motivation for research to identify and evaluate different psychological conditions.
Erhan Bergil
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Aggregate Kernel Inverse Regression Estimation
Mathematics, 2023 Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors. Many existing SDR methods rely on some assumptions about the distribution of predictors. Wang et al.
Wenjuan Li +3 more
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