Results 31 to 40 of about 845,542 (252)
Sufficient dimension reduction via principal Lq support vector machine [PDF]
, 2016 Principal support vector machine was proposed recently by Li, Artemiou and Li (2011) to combine L$1$ support vector machine and sufficient dimension reduction.
Artemiou, Andreas, Dong, Yuexiao
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Penalized principal logistic regression for sparse sufficient dimension reduction [PDF]
, 2017 Sufficient dimension reduction (SDR) is a successive tool for reducing the dimensionality of predictors by finding the central subspace, a minimal subspace of predictors that preserves all the regression information. When predictor dimension is large, it
Artemiou, Andreas, Shin, Seung Jun
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The reduction of the closest disentangled states [PDF]
, 2002 We study the closest disentangled state to a given entangled state in any system (multi-party with any dimension). We obtain the set of equations the closest disentangled state must satisfy, and show that its reduction is strongly related to the extremal
Galvão E F +6 more
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Fusing sufficient dimension reduction with neural networks
Computational Statistics & Data Analysis, 2022 19 pages, 4 figures, 10 ...
Kapla, Daniel +2 more
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Patient perceived quality of nursing care in hemodialysis: A meta-synthesis
Healthcare in Low-resource Settings, 2021 This study was done with the purpose of clarifying the concept of patient perceived quality of nursing care in hemodialysis. In this meta-synthesis study, qualitative studies was searched in the four interntional databases from January 1st, 2000 to ...
Abbas Balouchi +3 more
doaj +1 more source
Lattice Attacks on NTRU Revisited
IEEE Access, 2021 NTRU cryptosystem was proposed by J. Hoffstein, J.Pipher and J.H. Silverman in 1996, whose security is related to the hardness of finding sufficient short vectors in NTRU lattice with dimension $2N$ .
Jingguo Bi, Lidong Han
doaj +1 more source
Structure of equilibrium states on self-affine sets and strict monotonicity of affinity dimension [PDF]
, 2017 A fundamental problem in the dimension theory of self-affine sets is the construction of high-dimensional measures which yield sharp lower bounds for the Hausdorff dimension of the set.
Käenmäki, Antti, Morris, Ian D.
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PCA consistency in high dimension, low sample size context [PDF]
, 2009 Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows [i.e., High Dimension, Low ...
Jung, Sungkyu, Marron, J. S.
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Sufficient dimension reduction and prediction in regression [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009 Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most ...
Adragni, Kofi P., Cook, R. Dennis
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Conditional variance estimator for sufficient dimension reduction
Bernoulli, 2022 23 pages, 3 ...
Fertl, Lukas, Bura, Efstathia
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