Results 1 to 10 of about 4,885,621 (179)

Integrative sparse partial least squares [PDF]

Statistics in Medicine, 2021
Partial least squares, as a dimension reduction technique, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variables may weaken estimation performance, the sparse partial least squares (SPLS) technique has been proposed to identify important variables and generate more interpretable ...
Weijuan Liang, Shuangge Ma, Qingzhao Zhang, Tingyu Zhu   +3 more
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Extreme partial least-squares

Journal of Multivariate Analysis, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bousebata, Meryem, Enjolras, Geoffroy, Girard, Stéphane   +2 more
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Bayesian Sparse Partial Least Squares [PDF]

Neural Computation, 2013
Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years.
Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes   +4 more
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Partial least squares for face hashing [PDF]

Neurocomputing, 2016
Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability.
dos Santos, Cassio, Kijak, Ewa, Robson Schwartz, William, Gravier, Guillaume   +3 more
openaire   +1 more source

Penalized partial least squares for pleiotropy [PDF]

BMC Bioinformatics, 2021
AbstractBackgroundThe increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some ...
Camilo Broc, Thérèse Truong, Benoit Liquet   +2 more
openaire   +5 more sources

Partial least squares for dependent data [PDF]

Biometrika, 2016
The partial least squares algorithm for dependent data realisations is considered. Consequences of ignoring the dependence for the algorithm performance are studied both theoretically and in simulations. It is shown that ignoring certain non-stationary dependence structures leads to inconsistent estimation.
Singer, Marco, Krivobokova, Tatyana, Munk, Axel, Groot, Bert L. de   +3 more
openaire   +5 more sources

PLASMA: Partial LeAst Squares for Multiomics Analysis

Cancers, 2023
Background/Objectives: Recent growth in the number and applications of high-throughput “omics” technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. Methods: Here we present the first algorithm, PLASMA, that can learn to
Kyoko Yamaguchi, Salma Abdelbaky, Lianbo Yu, Christopher C. Oakes, Lynne V. Abruzzo, Kevin R. Coombes   +5 more
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Envelopes and Partial Least Squares Regression

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2013
SummaryWe build connections between envelopes, a recently proposed context for efficient estimation in multivariate statistics, and multivariate partial least squares (PLS) regression. In particular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which opens the door to pursuing the same goals as PLS but using ...
Cook, R. D., Helland, I. S., Su, Z.
openaire   +1 more source

Shrinkage for Extreme Partial Least-Squares

Statistics and Computing
Abstract This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector.
Arbel, Julyan, Girard, Stéphane, Lorenzo, Hadrien   +2 more
openaire   +6 more sources

Robust Partially-Compressed Least-Squares

CoRR, 2015
Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off.
Stephen Becker, Ban Kawas, Marek Petrik, Karthikeyan Natesan Ramamurthy   +3 more
openaire   +3 more sources