Results 11 to 20 of about 281,586 (259)
Dimension Reduction for Fréchet Regression [PDF]
Journal of the American Statistical Association, 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.
Qi Zhang, Lingzhou Xue, Bing Li
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Sufficient Dimension Reduction: An Information-Theoretic Viewpoint. [PDF]
Entropy (Basel), 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 ...
Ghosh D.
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Spatially aware dimension reduction for spatial transcriptomics. [PDF]
Nat Commun, 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 ...
Shang L, Zhou X.
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Dimension reduction of noisy interacting systems
Physical Review Research, 2023 We consider a class of models describing an ensemble of identical interacting agents subject to multiplicative noise. In the thermodynamic limit, these systems exhibit continuous and discontinuous phase transitions in a, generally, nonequilibrium setting.
Niccolò Zagli +3 more
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Dimension Reduction Regression in R
Journal of Statistical Software, 2002 Regression is the study of the dependence of a response variable y on a collection predictors p collected in x. In dimension reduction regression, we seek to find a few linear combinations β1x,...,βdx, such that all the information about the regression ...
Sanford Weisberg
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Cumulative Median Estimation for Sufficient Dimension Reduction
Stats, 2021 In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when ...
Stephen Babos, Andreas Artemiou
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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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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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Tensor sufficient dimension reduction [PDF]
WIREs Computational Statistics, 2015 Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example.
Zhong, Wenxuan +2 more
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