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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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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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Persuasion by Dimension Reduction [PDF]
SSRN Electronic Journal, 2021 This paper has been replaced and subsumed by arXiv:2210.00637.
Semyon Malamud, Andreas Schrimpf
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Sufficient Dimension Reduction: An Information-Theoretic Viewpoint
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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A Review on Dimension Reduction [PDF]
International Statistical Review, 2012 RésuméRésumer l'impact d'un nombre élevé de variables explicatives à celui d'un nombre réduit de combinaisons linéaires bien choisies constitue une façon efficace de réduire la dimension d'un problème. Cette réduction à un petit nombre de combinaisons linéaires est réalisée à partir d'hypothèses minimales sur la forme de la dépendance et jouit, par ...
Ma, Yanyuan, Zhu, Liping
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J. Mach. Learn. Res., 2023 The multivariate adaptive regression spline (MARS) is one of the popular estimation methods for nonparametric multivariate regressions. However, as MARS is based on marginal splines, to incorporate interactions of covariates, products of the marginal splines must be used, which leads to an unmanageable number of basis functions when the order of ...
Liu, Yu, Li, Degui, Xia, Yingcun
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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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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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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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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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