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Multiple imputation using dimension reduction techniques for high-dimensional data [article]

Domonique W. Hodge and Sandra E. Safo and Qi Long
2019 arXiv   pre-print
Multiple imputation (MI) is one of the most widely used methods for handling missing data which can be partly attributed to its ease of use.  ...  dimension reduction (SDR) techniques.  ...  Zhao and Long 7 proposed an MI approach for high-dimensional data based on regularized regression that does account for the uncertainty in imputation.  ... 
arXiv:1905.05274v1 fatcat:j2aitqxsbvedlakydvdxjliliy

A Review on Dimension Reduction

Yanyuan Ma, Liping Zhu
2012 International Statistical Review  
Because the replacement of high-dimensional covariates by low-dimensional linear combinations is performed with a minimum assumption on the specific regression form, it enjoys attractive advantages as  ...  We review the current literature of dimension reduction with an emphasis on the two most popular models, where the dimension reduction affects the conditional distribution and the conditional mean, respectively  ...  Acknowledgements The authors thank the editor's interest in dimension reduction which initiated this review.  ... 
doi:10.1111/j.1751-5823.2012.00182.x pmid:23794782 pmcid:PMC3685755 fatcat:lin3oozxgzezlokipchefowen4

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares

Édith Le Floch, Vincent Guillemot, Vincent Frouin, Philippe Pinel, Christophe Lalanne, Laura Trinchera, Arthur Tenenhaus, Antonio Moreno, Monica Zilbovicius, Thomas Bourgeron, Stanislas Dehaene, Bertrand Thirion (+2 others)
2012 NeuroImage  
Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies.  ...  We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes  ...  Acknowledgments This work was supported by CEA and the Karametria grant for the French National Agency for Research (ANR).  ... 
doi:10.1016/j.neuroimage.2012.06.061 pmid:22781162 fatcat:ddcca3dzmfa67ajmm2glhj44ga

Horseshoe Prior Bayesian Quantile Regression [article]

David Kohns, Tibor Szendrei
2021 arXiv   pre-print
The performance of the proposed HS-BQR is evaluated on Monte Carlo simulations and a high dimensional Growth-at-Risk (GaR) forecasting application for the U.S.  ...  This paper extends the horseshoe prior of Carvalho et al. (2010) to Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm for computation in high dimensions.  ...  However, a computational bottleneck is present in very high dimensions in evaluating the K × K dimensional inverse for the conditional posterior of β.  ... 
arXiv:2006.07655v2 fatcat:o5m64l2aqrebba7u7bnydgizyu

Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge

Konstantinos Slavakis, Georgios B. Giannakis, Gonzalo Mateos
2014 IEEE Signal Processing Magazine  
The dimensionality reduction module of [3] is based on local linear embedding (LLE) principles [61] , which assume that the observed { } y V !  ...  disciplines such as 1) statistics, for inference and prediction [28] , 2) machine learning, for classification, regression, clustering, and dimensionality reduction [63] , and 3) SP, as well as (non  ...  This term is also instrumental for accommodating missing entries in ( ) .  ... 
doi:10.1109/msp.2014.2327238 fatcat:4rhash677fb7lcp7acnxxknsfi

Review of Dimension Reduction Methods

Salifu Nanga, Ahmed Tijani Bawah, Benjamin Ansah Acquaye, Mac-Issaka Billa, Francis Delali Baeta, Nii Afotey Odai, Samuel Kwaku Obeng, Ampem Darko Nsiah
2021 Journal of Data Analysis and Information Processing  
Sufficient dimension reduction (SDR) techniques are being explored recently, with Li proposing the first technique, the seminal sliced inverse regression.  ...  [37] proposed the Generalized Power Sparse PCA (GP-SPCA) which was developed to overcome the curse of dimensionality issue of Dimension Reduction.  ... 
doi:10.4236/jdaip.2021.93013 fatcat:tlgvjk6xzbfe5gristkd7ww4tq

A kernel approach to parallel MRI reconstruction

Yuchou Chang, Dong Liang, Leslie Ying
2011 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Specifically, the acquired kspace data are mapped through a nonlinear transform to a high-dimensional space and then linearly combined to estimate the missing k-space data.  ...  It linearly combines the acquired k-space signals to estimate the missing k-space signals where the coefficients are obtained by linear regression using auto-calibration signals.  ...  Steve, "The mathematics of learning: dealing with data," Notices of the American Mathematical Society, vol. 50, pp. 537-544, May, 2003.  ... 
doi:10.1109/isbi.2011.5872430 dblp:conf/isbi/ChangLY11 fatcat:gkswoyd2zrhkpnwypx5jvwnb2e

Motion estimated and compensated compressed sensing dynamic magnetic resonance imaging: What we can learn from video compression techniques

Hong Jung, Jong Chul Ye
2010 International journal of imaging systems and technology (Print)  
Compressed sensing has become an extensive research area in MR community because of the opportunity for unprecedented high spatio-temporal resolution reconstruction.  ...  Because dynamic magnetic resonance imaging (MRI) usually has huge redundancy along temporal direction, compressed sensing theory can be effectively used for this application.  ...  Jaeseok Park at Yonsei University Medical center for providing us in vivo data.  ... 
doi:10.1002/ima.20231 fatcat:6cih7e6kpjgmpk3ypal25yskju

KerNL: Kernel-based NonLinear Approach to Parallel MRI Reconstruction

Jingyuan Lyu, Ukash Nakarmi, Dong Liang, Jinhua Sheng, Leslie Ying
2018 IEEE Transactions on Medical Imaging  
The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are  ...  Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging.  ...  Dimensionality reduction involves projecting data from a highdimensional space to a lower-dimensional one without a significant loss of information.  ... 
doi:10.1109/tmi.2018.2864197 pmid:30106676 pmcid:PMC6422679 fatcat:x2vcz2wbojbcplee5mnxusiquu

Challenges and opportunities in high-dimensional choice data analyses

Prasad Naik, Michel Wedel, Lynd Bacon, Anand Bodapati, Eric Bradlow, Wagner Kamakura, Jeffrey Kreulen, Peter Lenk, David M. Madigan, Alan Montgomery
2008 Marketing letters  
For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics.  ...  Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets  ...  Inverse regression methods A broad class of methods for dimension reduction (see Naik et al. 2000) is based on the work by Li (1991) on sliced inverse regression.  ... 
doi:10.1007/s11002-008-9036-3 fatcat:tna2cfftxrf7tihtc5f3y6oepu

Fast Randomized Algorithms for t-Product Based Tensor Operations and Decompositions with Applications to Imaging Data [article]

Davoud Ataee Tarzanagh, George Michailidis
2018 arXiv   pre-print
The performance of the proposed algorithms is illustrated on diverse imaging applications, including mass spectrometry data and image and video recovery from incomplete and noisy data.  ...  The proposed tubal focused algorithms employ a small number of lateral and/or horizontal slices of the underlying 3-rd order tensor, that come with relative error guarantees for the quality of the obtained  ...  The authors would like to thank the Associate Editor and three anonymous referees for many constructive comments and suggestions that improved significantly the structure and readability of the paper.  ... 
arXiv:1704.04362v4 fatcat:g7asxan2xjatxnvf6oqkyavwoy

An Efficient Convex Formulation for Reduced-Rank Linear Discriminant Analysis in High Dimensions

Jing Zeng, Xin Zhang, Qing Mai
2023 Statistica sinica  
A sparse dimension reduction subspace is constructed to contain all the necessary information for linear discriminant analysis.  ...  In this paper, we propose a parsimonious reduced-rank linear discriminant analysis model for high-dimensional sparse multi-class discriminant analysis.  ...  Research for this paper was supported in part by grants  ... 
doi:10.5705/ss.202021.0047 fatcat:qvshgp66qfhr7pnjf4zdcxuqiq

Multilocus Genetic Analysis of Brain Images

Derrek P. Hibar, Omid Kohannim, Jason L. Stein, Ming-Chang Chiang, Paul M. Thompson
2011 Frontiers in Genetics  
These complex data types require new methods for data reduction and joint consideration of the image and the genome.  ...  Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional.  ...  An extension of PCReg and other data reduction techniques is to perform data reduction on both the genome and the 3D brain imaging traits.  ... 
doi:10.3389/fgene.2011.00073 pmid:22303368 pmcid:PMC3268626 fatcat:gwzt3dvuvndrhjdrykmbeyodj4

Generative adversarial networks improve interior computed tomography angiography reconstruction

Juuso Heikki Jalmari Ketola, Helinä Heino, Mikael Asko Kaarlo Juntunen, Miika T Nieminen, Samuli Siltanen, Satu Irene Inkinen
2021 Biomedical engineering and physics express  
In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions.  ...  Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output).  ...  The authors thank NVIDIA Corporation for donating the Quadro P6000 GPU used in this study. Pasi Sepponen is gratefully acknowledged for collecting the data used in this study.  ... 
doi:10.1088/2057-1976/ac31cb pmid:34673559 fatcat:2dbzienx6fhw3bc7zibv6her7a

Groupwise Dimension Reduction via Envelope Method

Zifang Guo, Lexin Li, Wenbin Lu, Bing Li
2015 Journal of the American Statistical Association  
The family of sufficient dimension reduction (SDR) methods that produce informative combinations of predictors, or indices, are particularly useful for high dimensional regression analysis.  ...  Dimension reduction methods are particularly useful for this type of data analysis, by first producing low dimensional but informative summary features, and then feeding them into downstream analysis.  ...  Acknowledgments The authors are grateful to three referees and an Associate Editor for their many useful comments and suggestions, which have helped to greatly improve on an earlier manuscript.  ... 
doi:10.1080/01621459.2014.970687 pmid:26973362 pmcid:PMC4787236 fatcat:wfzrtaur7ffi7bxns4wq4xckw4
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