Li Wang

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 25

Top Topics

Dimensionality Reduction

Partial Least Squares

Structure Learning

High Dimensional

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Qin Li, Li Wang, Yunan Yang
Differential Equation-Constrained Optimization with Stochasticity. SIAM/ASA J. Uncertain. Quantification 12 (2) (2024)
Li Wang, Haian Yin, Jin Zhang
Density-Based Distance Preserving Graph: Theoretical and Practical Analyses. IEEE Trans. Neural Networks Learn. Syst. 34 (9) (2023)
Li Wang, Lei-Hong Zhang, Ren-Cang Li
Trace ratio optimization with an application to multi-view learning. Math. Program. 201 (1) (2023)
Li Wang, Ren-Cang Li, Wen-Wei Lin
Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions. IEEE Trans. Neural Networks Learn. Syst. 34 (8) (2023)
Lei-Hong Zhang, Li Wang, Zhaojun Bai, Ren-Cang Li
A Self-Consistent-Field Iteration for Orthogonal Canonical Correlation Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Xijun Ma, Chungen Shen, Li Wang, Lei-Hong Zhang, Ren-Cang Li
A self-consistent-field iteration for MAXBET with an application to multi-view feature extraction. Adv. Comput. Math. 48 (2) (2022)
Lu Zhang, Li Wang, Dajiang Zhu, Alzheimer's Disease Neuroimaging Initiative
Predicting brain structural network using functional connectivity. Medical Image Anal. 79 (2022)
Li Wang, Ren-Cang Li
A Scalable Algorithm for Large-Scale Unsupervised Multi-View Partial Least Squares. IEEE Trans. Big Data 8 (4) (2022)
Zitong Wang, Li Wang, Raymond H. Chan, Tieyong Zeng
Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning. ECML/PKDD (4) (2022)
Hok Shing Wong, Li Wang, Raymond H. Chan, Tieyong Zeng
Deep Tensor CCA for Multi-View Learning. IEEE Trans. Big Data 8 (6) (2022)
Feng Liu, Li Wang, Yifei Lou, Ren-Cang Li, Patrick L. Purdon
Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors. IEEE Trans. Medical Imaging 40 (1) (2021)
Yi Xue, Wenjian Qin, Chen Luo, Pengfei Yang, Yangkang Jiang, Tiffany Tsui, Hongjian He, Li Wang, Jiale Qin, Yaoqin Xie, Tianye Niu
Multi-Material Decomposition for Single Energy CT Using Material Sparsity Constraint. IEEE Trans. Medical Imaging 40 (5) (2021)
Li Wang, Raymond H. Chan, Tieyong Zeng
Probabilistic Semi-Supervised Learning via Sparse Graph Structure Learning. IEEE Trans. Neural Networks Learn. Syst. 32 (2) (2021)
Lu Zhang, Li Wang, Jean Gao, Shannon L. Risacher, Jingwen Yan, Gang Li, Tianming Liu, Dajiang Zhu
Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Medical Image Anal. 72 (2021)
Li Wang, Ren-Cang Li
Learning Low-Dimensional Latent Graph Structures: A Density Estimation Approach. IEEE Trans. Neural Networks Learn. Syst. 31 (4) (2020)
Hok Shing Wong, Li Wang, Raymond H. Chan, Tieyong Zeng
Deep Tensor CCA for Multi-view Learning. CoRR (2020)
Feng Liu, Li Wang, Yifei Lou, Ren-Cang Li, Patrick L. Purdon
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior. CoRR (2019)
Zitong Wang, Li Wang, Raymond H. Chan, Tieyong Zeng
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points. CoRR (2019)
Li Wang, Qi Mao
Probabilistic Dimensionality Reduction via Structure Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41 (1) (2019)
Li Wang, Qi Mao, Ivor W. Tsang
Latent Smooth Skeleton Embedding. AAAI (2017)
Jiezhang Cao, Qingyao Wu, Yuguang Yan, Li Wang, Mingkui Tan
On the Flatness of Loss Surface for Two-layered ReLU Networks. ACML (2017)
Qi Mao, Li Wang, Ivor W. Tsang, Yijun Sun
Principal Graph and Structure Learning Based on Reversed Graph Embedding. IEEE Trans. Pattern Anal. Mach. Intell. 39 (11) (2017)
Qi Mao, Li Wang, Ivor W. Tsang
A unified probabilistic framework for robust manifold learning and embedding. Mach. Learn. 106 (5) (2017)
Li Wang
Probabilistic Dimensionality Reduction via Structure Learning. CoRR (2016)
Mingkui Tan, Yan Yan, Li Wang, Anton van den Hengel, Ivor W. Tsang, Qinfeng (Javen) Shi
Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data. AAAI (2016)
Qi Mao, Li Wang, Steve Goodison, Yijun Sun
Dimensionality Reduction Via Graph Structure Learning. KDD (2015)
Mingkui Tan, Ivor W. Tsang, Li Wang
Matching Pursuit LASSO Part II: Applications and Sparse Recovery Over Batch Signals. IEEE Trans. Signal Process. 63 (3) (2015)
Qi Mao, Ivor W. Tsang, Shenghua Gao, Li Wang
Generalized Multiple Kernel Learning With Data-Dependent Priors. IEEE Trans. Neural Networks Learn. Syst. 26 (6) (2015)
Qi Mao, Wei Zheng, Li Wang, Yunpeng Cai, Volker Mai, Yijun Sun
Parallel Hierarchical Clustering in Linearithmic Time for Large-Scale Sequence Analysis. ICDM (2015)
Qi Mao, Li Wang, Ivor W. Tsang, Yijun Sun
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation. CoRR (2015)
Qi Mao, Le Yang, Li Wang, Steve Goodison, Yijun Sun
SimplePPT: A Simple Principal Tree Algorithm. SDM (2015)
Mingkui Tan, Ivor W. Tsang, Li Wang
Matching Pursuit LASSO Part I: Sparse Recovery Over Big Dictionary. IEEE Trans. Signal Process. 63 (3) (2015)
Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan
Riemannian Pursuit for Big Matrix Recovery. ICML (2014)
Mingkui Tan, Ivor W. Tsang, Li Wang
Towards ultrahigh dimensional feature selection for big data. J. Mach. Learn. Res. 15 (1) (2014)
Mingkui Tan, Ivor W. Tsang, Li Wang
Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection. IEEE Trans. Neural Networks Learn. Syst. 24 (10) (2013)
Mingkui Tan, Ivor W. Tsang, Li Wang
Is Matching Pursuit Solving Convex Problems? CoRR (2013)
Mingkui Tan, Ivor W. Tsang, Li Wang, Xinming Zhang
Convex Matching Pursuit for Large-Scale Sparse Coding and Subset Selection. AAAI (2012)
Mingkui Tan, Ivor W. Tsang, Li Wang
Towards Large-scale and Ultrahigh Dimensional Feature Selection via Feature Generation CoRR (2012)
Mingkui Tan, Li Wang, Ivor W. Tsang
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. ICML (2010)