Li Wang

Haian Yin

Jin Zhang

IEEE Trans. Neural Networks Learn. Syst., September, 2023

Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions.

[BibT_eX]

[DOI]

Wen-Wei Lin

IEEE Trans. Neural Networks Learn. Syst., August, 2023

Trace ratio optimization with an application to multi-view learning.

[BibT_eX]

[DOI]

Lei-Hong Zhang

Math. Program., 2023

Exploring the Influence of Information Entropy Change in Learning Systems.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Deep Tensor CCA for Multi-View Learning.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2022

A Scalable Algorithm for Large-Scale Unsupervised Multi-View Partial Least Squares.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2022

A Self-Consistent-Field Iteration for Orthogonal Canonical Correlation Analysis.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Maximizing sum of coupled traces with applications.

[BibT_eX]

[DOI]

Lei-Hong Zhang

Steven J. A. van der Werff

Numerische Mathematik, 2022

A comparison of methods to harmonize cortical thickness measurements across scanners and sites.

[BibT_eX]

[DOI]

Sophia I. Thomopoulos

Faisal Rashid

Christopher R. K. Ching

Benjamin Suarez-Jimenez

Sanne J. H. van Rooij

Robert R. J. M. Vermeiren

Mayuresh S. Korgaonkar

Nicholas D. Davenport

Daniel W. Grupe

Jack B. Nitschke

Terri A. deRoon-Cassini

Jacklynn M. Fitzgerald

Nic J. A. van der Wee

NeuroImage, 2022

Predicting brain structural network using functional connectivity.

[BibT_eX]

[DOI]

Alzheimer's Disease Neuroimaging Initiative

Medical Image Anal., 2022

Orthogonal multi-view analysis by successive approximations via eigenvectors.

[BibT_eX]

[DOI]

Neurocomputing, 2022

A self-consistent-field iteration for MAXBET with an application to multi-view feature extraction.

[BibT_eX]

[DOI]

Adv. Comput. Math., 2022

Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Probabilistic Semi-Supervised Learning via Sparse Graph Structure Learning.

[BibT_eX]

[DOI]

Raymond H. Chan

Tieyong Zeng

IEEE Trans. Neural Networks Learn. Syst., 2021

Multi-Material Decomposition for Single Energy CT Using Material Sparsity Constraint.

[BibT_eX]

[DOI]

IEEE Trans. Medical Imaging, 2021

Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors.

[BibT_eX]

[DOI]

IEEE Trans. Medical Imaging, 2021

Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment.

[BibT_eX]

[DOI]

Medical Image Anal., 2021

On Generalizing Trace Minimization.

[BibT_eX]

[DOI]

CoRR, 2021

Representing Alzheimer's Disease Progression via Deep Prototype Tree.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Learning Low-Dimensional Latent Graph Structures: A Density Estimation Approach.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

Orthogonal canonical correlation analysis and applications.

[BibT_eX]

[DOI]

Optim. Methods Softw., 2020

Spectral norm of a symmetric tensor and its computation.

[BibT_eX]

[DOI]

Shmuel Friedland

Math. Comput., 2020

Uncorrelated Semi-paired Subspace Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions.

[BibT_eX]

[DOI]

Wen-Wei Lin

CoRR, 2020

Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative Adversarial Network.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Jointly Analyzing Alzheimer's Disease Related Structure-Function Using Deep Cross-Model Attention Network.

[BibT_eX]

[DOI]

Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

Learning Latent Structure Over Deep Fusion Model of Mild Cognitive Impairment.

[BibT_eX]

[DOI]

Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

2019

Probabilistic Dimensionality Reduction via Structure Learning.

[BibT_eX]

[DOI]

Qi Mao

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points.

[BibT_eX]

[DOI]

CoRR, 2019

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior.

[BibT_eX]

[DOI]

CoRR, 2019

A Cascaded Multi-modality Analysis in Mild Cognitive Impairment.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

Accessing Latent Connectome of Mild Cognitive Impairment via Discriminant Structure Learning.

[BibT_eX]

[DOI]

Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019

Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning.

[BibT_eX]

[DOI]

Paul M. Thompson

Proceedings of the Information Processing in Medical Imaging, 2019

2018

Exploring latent structures of Alzheimer's disease via structure learning.

[BibT_eX]

[DOI]

Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

2017

Principal Graph and Structure Learning Based on Reversed Graph Embedding.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2017

A unified probabilistic framework for robust manifold learning and embedding.

[BibT_eX]

[DOI]

Qi Mao

Mach. Learn., 2017

On the Flatness of Loss Surface for Two-layered ReLU Networks.

[BibT_eX]

[DOI]

Proceedings of The 9th Asian Conference on Machine Learning, 2017

Latent Smooth Skeleton Embedding.

[BibT_eX]

[DOI]

Qi Mao

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Probabilistic Dimensionality Reduction via Structure Learning.

[BibT_eX]

[DOI]

CoRR, 2016

Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Matching Pursuit LASSO Part II: Applications and Sparse Recovery Over Batch Signals.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2015

Matching Pursuit LASSO Part I: Sparse Recovery Over Big Dictionary.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2015

Generalized Multiple Kernel Learning With Data-Dependent Priors.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2015

A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation.

[BibT_eX]

[DOI]

CoRR, 2015

SimplePPT: A Simple Principal Tree Algorithm.

[BibT_eX]

[DOI]

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Dimensionality Reduction Via Graph Structure Learning.

[BibT_eX]

[DOI]

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

2014

Towards ultrahigh dimensional feature selection for big data.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2014

Riemannian Pursuit for Big Matrix Recovery.

[BibT_eX]

[DOI]

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2013

Is Matching Pursuit Solving Convex Problems?

[BibT_eX]

[DOI]

CoRR, 2013

2012

Towards Large-scale and Ultrahigh Dimensional Feature Selection via Feature Generation

[BibT_eX]

[DOI]

CoRR, 2012

Convex Matching Pursuit for Large-Scale Sparse Coding and Subset Selection.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2010

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets.

[BibT_eX]

[DOI]