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3.0

Jun 30, 2018
06/18

by
Xiang Zhang; Lexin Li; Hua Zhou; Dinggang Shen; the Alzheimer's Disease Neuroimaging Initiative

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In an increasing number of neuroimaging studies, brain images, which are in the form of multidimensional arrays (tensors), have been collected on multiple subjects at multiple time points. Of scientific interest is to analyze such massive and complex longitudinal images to diagnose neurodegenerative disorders and to identify disease relevant brain regions. In this article, we treat those problems in a unifying regression framework with image predictors, and propose tensor generalized estimating...

Topics: Statistics, Methodology

Source: http://arxiv.org/abs/1412.6592

6
6.0

Jun 29, 2018
06/18

by
Will Wei Sun; Lexin Li

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Motivated by applications in neuroimaging analysis, we propose a new regression model with a tensor response and a vector predictor. The model embeds two key sparse structures: element-wise sparsity and low-rankness. It can handle both a general and a symmetric tensor response, and thus is applicable to both structural and functional neuroimaging data. We formulate the model parameter estimation as a non-convex optimization problem, and develop an efficient alternating updating algorithm. We...

Topics: Machine Learning, Methodology, Applications, Statistics

Source: http://arxiv.org/abs/1609.04523

3
3.0

Jun 28, 2018
06/18

by
Yin Xia; Lexin Li

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Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neurons and brain regions, and links represent statistical dependences that are often encoded in terms of partial correlations. Such a graph is inferred from matrix-valued neuroimaging data such as electroencephalography and functional magnetic resonance imaging. There have been a good number of successful proposals for...

Topics: Methodology, Statistics

Source: http://arxiv.org/abs/1511.00718

51
51

Jul 20, 2013
07/13

by
Bing Li; Andreas Artemiou; Lexin Li

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We introduce a principal support vector machine (PSVM) approach that can be used for both linear and nonlinear sufficient dimension reduction. The basic idea is to divide the response variables into slices and use a modified form of support vector machine to find the optimal hyperplanes that separate them. These optimal hyperplanes are then aligned by the principal components of their normal vectors. It is proved that the aligned normal vectors provide an unbiased, $\sqrt{n}$-consistent, and...

Source: http://arxiv.org/abs/1203.2790v1

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Sep 19, 2013
09/13

by
Lexin Li; Christopher J. Nachtsheim

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Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]

Source: http://arxiv.org/abs/0708.3779v1

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58

Sep 21, 2013
09/13

by
Hua Zhou; Lexin Li

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Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry, and electroencephalography, matrix type covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates are needed, and sparsity or other forms of regularization are crucial due to the ultrahigh...

Source: http://arxiv.org/abs/1204.3331v1

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Jul 20, 2013
07/13

by
Xiaoshan Li; Hua Zhou; Lexin Li

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Large-scale neuroimaging studies have been collecting brain images of study individuals, which take the form of two-dimensional, three-dimensional, or higher dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a long vector causes extremely high dimensionality that compromises classical regression methods, and, more seriously, destroys...

Source: http://arxiv.org/abs/1304.5637v1

Episodes grouped by series

Topics: UC Berkeley, Berkeley, Cal, webcast.berkeley, iTunes U, Public Health 245, Fall 2014

13
13

Jun 26, 2018
06/18

by
Lexin Li; Xin Zhang

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Aiming at abundant scientific and engineering data with not only high dimensionality but also complex structure, we study the regression problem with a multidimensional array (tensor) response and a vector predictor. Applications include, among others, comparing tensor images across groups after adjusting for additional covariates, which is of central interest in neuroimaging analysis. We propose parsimonious tensor response regression adopting a generalized sparsity principle. It models all...

Topics: Methodology, Statistics

Source: http://arxiv.org/abs/1501.07815

128
128

Jul 20, 2013
07/13

by
Hua Zhou; Lexin Li; Hongtu Zhu

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Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models...

Source: http://arxiv.org/abs/1203.3209v1