Selected Publications

(google scholar)

  1. Multi-view Partially Shared Subspace Learning (with Xijun Ma, Leihong Zhang, Chungen Shen and Rencang Li). Submitted, 2021.
  2. Uncorrelated Semi-paired Subspace Learning (with Leihong Zhang, Chungen Shen and Ren-cang Li). Submitted, 2020. ([arxiv ])
  3. Disease2Vec: Representing Alzheimer’s Disease Progression via Disease Embedding Tree (with Lu Zhang, Tianming Liu and Dajiang Zhu). Pharmacological Research, 2023. ([arxiv ])
  4. Maximizing  Sum of Coupled Traces with Applications (with Leihong Zhang and Rencang Li), Numerische Mathematik, to appear, 2022.
  5. On Generalizing Trace Minimization (with Xin Liang, Leihong Zhang and Ren-cang Li). Linear Algebra and Its Applications, to appear, 2022.([arxiv ])
  6. Trace Ratio Optimization with an Application to Multi-view Learning (with Leihong Zhang and Ren-cang Li). Mathematical Programming, to appear, 2022. ([arxiv ])
  7. Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors (with Leihong Zhang, Chungen Shen and Ren-cang Li). Neurocomputing, to appear, 2022. ([arxiv ])
  8. Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning (with Zitong Wang, Raymond Chan and Tieyong Zeng). ECML PKDD, to appear, 2022. ([ arxiv ])
  9. Nearly Optimal Stochastic Approximation for Online Principal Subspace Estimation (with Xin Liang, Zhen-Chen Guo, Ren-Cang Li and Wen-Wei Lin). SCIENCE CHINA Mathematics, to appear, 2022.
  10. De-noising Spatial Expression Profiling Data Based on in situ Position and Image Information (with Yunguan Wang, Yang Xie, Tao Wang et al.). Nature Method, to appear, 2022. [biorxiv]
  11. Interpreting the B Cell Receptor Repertoire with Single Cell Gene Expression (with Tao Wang et al.). Nature Machine Intelligence, to appear, 2022.
  12. Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer (with Jing Yang, Tianye Niu and Ren-cang Li). Physics in Medicine and Biology, to appear, 2022.
  13. Predicting Brain Structural Network using Functional Connectivity (with Lu Zhang, Xiaowei Yu, Yanjun Lyu and Dajiang Zhu), Medical Imaging Analysis, to appear, 2022.
  14. A Self-Consistent-Field Iteration for MAXBET With an Application to Multi-view Feature Extraction (with Xijun Ma, Chungen Shen, Leihong Zhang and Rencang Li). Advances in Computational Mathematics, to appear, 2022.
  15. Higher Order Correlation Analysis for Multiview Learning (with Jiawang Nie and Zequn Zheng). Pacific Journal of Optimization, to appear, 2022.
  16. Mapping Transcriptomic Vector Fields of Single Cells (with Xiaojie Qiu, Jonathan S Weissman et al.). Cell, To appear, 2021. [biorxiv]
  17. Density-based Distance Preserving Graph for Graph-based Learning (with Haian Yin and Jin Zhang). IEEE Transactions on Neural Networks and Learning Systems (TNNLS). To appear, 2021.
  18. Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions (with Ren-cang Li and Wen-wei Lin). IEEE Transactions on Neural Networks and Learning Systems (TNNLS). To appear, 2021. ([ arxiv ])
  19. A Dual Active-set Proximal Newton Algorithm for Sparse Approximation of Correlation Matrices (with Xiao Liu and Chungen Shen). Optimization Methods and Software. To appear, 2021.
  20. Highly accurate Latouche-Ramaswami logarithmic reduction algorithm for quasi-birth-and-death process (with Guiding Gu and Ren-cang Li). Journal of Mathematical Study, To appear, 2021.
  21. A Lagrange Multiplier Expression Method for Bilevel Polynomial Optimization (with Jiawang Nie, Jane Ye and Shuhan Zhong). SIAM on Optimization, To appear, 2021. ([ arxiv ])
  22. Deep Tensor CCA for Multi-view Learning (with Hok Shing Wong, Raymond Chan, and Tieyong Zeng). IEEE Transactions on Big Data, To appear, 2021. ([ arxiv ])([ link ])([code])
  23. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment (with Lu Zhang, Jean Gao, Jingwen Yan, Dajiang Zhu and Tianming Liu et. al.). Medical Image Analysis, to appear, 2021.
  24. Multi-material decomposition for single energy CT using material sparsity constraint (with Yi Xue, Tianye Niu et. al. ). IEEE Transactions on Medical Imaging, to appear, 2021.
  25. Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-cell RNA Sequencing Data (with Tianshi Lu, Seongoh Park, Sherry Wang, Tao Wang et al.). Cell Reports, to appear, 2020.
  26. Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior (with Feng Liu, Yifei Lou, Rencang Li and Patrick L. Purdon). IEEE Transactions on Medical Imaging, to appear, 2020. [2019 INFORMS data mining session Best Paper Award (non-student track)][arxiv]([ link ])
  27. A Scalable Algorithm for Large-Scale Unsupervised Multi-view Partial Least Squares (with Ren-cang Li). IEEE Transactions on Big Data, to appear, 2020. ([ link ])
  28. A Self-consistent-field Iteration for Orthogonal Canonical Correlation Analysis (with Leihong Zhang, Zhaojun Bai and Ren-cang Li). IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear, 2020. ([ arxiv ]) ([ link ])
  29. Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN based Generative Adversarial Network (with Lu Zhang and Dajiang Zhu). MICCAI, to appear, 2020. (Lu Zhang was awarded MICCAI 2020 Young Scientist Award) 
  30. Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm (with Xinlong Ju, Feng Liu, Ning Wang and Wei-Jen Lee). Energy Conversion and Management. To appear, 2020.
  31. Probabilistic Semi-supervised Learning via Sparse Graph Structure Learning (with Raymond Chan and Tieyong Zeng). IEEE Transactions on Neural Networks and Learning Systems (TNNLS). To appear, 2020. ([ link ])
  32. Towards inferring causal gene regulatory networks from single cell expression measurements (with Xiaojie Qiu, Cole Trapnell et al.). Cell Systems. To appear, 2020. [biorxiv]
  33. Geometric measure of entanglement of symmetric d-qubits is polynomial-time computable (with Shmuel Friedland). Mathematics of Computation. To appear, 2020. ([ arxiv ])
  34. Learning Latent Structure Over Deep Fusion Model of Mild Cognitive Impairment (with Lu Zhang and Dajiang Zhu). IEEE International Symposium on Biomedical Imaging (ISBI’20), 2020.
  35. Jointly Analyzing Alzheimer’s Disease Related Structure-Function Using Deep Cross-Model Attention Network (with Lu Zhang and Dajiang Zhu). IEEE International Symposium on Biomedical Imaging (ISBI’20), 2020.
  36. Orthogonal Canonical Correlation Analysis and Applications (with Leihong Zhang, Zhaojun Bai and Rencang Li). Optimization Methods and Software. To appear, 2019. [arxiv]
  37. Wind Farm Layout Optimization based on Support Vector Regression Guided Genetic Algorithm with Consideration of Landowner’s Participation (with Xinlong Ju, Feng Liu and Wei-Jen Lee). Energy Conversion and Management. To appear, 2019. [code]
  38. Learning Low-dimensional Latent Graph Structures: A Density Estimation Approach. (with Rencang Li). IEEE Transactions on Neural Networks and Learning Systems (TNNLS). To appear, 2019. [code]
  39. Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning (with Paul M. Thompson and Dajiang Zhu). International Conference on Information Processing in Medical Imaging (IPMI’19), 2019.
  40. Accessing Latent Connectome of Mild Cognitive Impairment via Discriminant Structure Learning (with Lu Zhang and Dajiang Zhu). IEEE International Symposium on Biomedical Imaging (ISBI’19), 2019.
  41. Cascaded Multi-Modality Analysis in Mild Cognitive Impairment. (with Lu Zhang, Jingwen Yan and Dajiang Zhu). MICCAI, workshop, 2019.
  42. Exploring Latent Structures of Alzheimer’s Disease via Structure Learning (with Dajiang Zhu). IEEE International Symposium on Biomedical Imaging (ISBI’18), 2018.
  43. Efficient Test-time Predictor Learning with Group-based Budget (with Dajiang Zhu and Yujie Chi). AAAI, 2018.
  44. On the Flatness of Loss Surface for Two-layered ReLU Networks (with Jiezhang Cao and Mingkui Tan et al.). ACML, 2017.
  45. Bilevel Polynomial Programs and Semidefinite Relaxation Methods (with Jiawang Nie and Jane Ye). SIAM on Optimization. 2017. ([ arxiv ])
  46. Probabilistic Dimensionality Reduction via Structure Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019. [arxiv] [code]
  47. Reversed graph embedding resolves complex single-cell developmental trajectories (with Xiaojie Qiu, Cole Trapnell et al.). Nature Methods, 2017. [arxiv]
  48. Latent Smooth Skeleton Embedding (with Ivor W. Tsang). Proceedings of the 31th AAAI Conference on Artificial Intelligence. 2017. [code]
  49. A unified probabilistic framework for robust manifold learning and embedding (with Ivor W. Tsang). Machine Learning, 2016. [code]
  50. Principal Graph and Structure Learning Based on Reversed Graph Embedding (with Ivor W. Tsang et al.). IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017. [code]
  51. Semidefinite relaxations on tensor best rank-1 approximation (with Jiawang Nie). SIAM Journal on Matrix Analysis and Applications. Vol. 35, No. 3, pp. 1155-1179, 2014. (ICCM 2019 Best Paper Award) [code]
  52. Semidefinite relaxations for semi-infinite polynomial programming (with Feng Guo). Computational Optimization and Applications, 58(1), pp. 133-159, 2014.
  53. Regularization methods for SDP relaxations in large scale polynomial optimization (with Jiawang Nie). SIAM Journal on Optimization, Vol. 22, No.2, pp. 408-428, 2012. [code]
  54. Minimizing rational functions by exact Jacobian SDP relaxation applicable to finite singularities (with Feng Guo and Guangming Zhou). Journal of Global Optimization. Vol. 58, No.2, pp. 261-284, 2014.
  55. Sparse recovery over big dictionary part I: matching pursuit LASSO and theoretical analysis (with Mingkui Tan and Ivor W. Tsang). IEEE Transactions on Signal Processing, 63(3):727-741, 2015. [code]
  56. Sparse recovery over big dictionary part II: batch mode matching pursuit LASSO and applications (with Mingkui Tan and Ivor W. Tsang). IEEE Transactions on Signal Processing, 63(3):742-753, 2015. [code]
  57. Towards Ultrahigh Dimensional Feature Selection for Big Data (with Mingkui Tan and Ivor W. Tsang). Journal of Machine Learning Research (JMLR). Vol.15, pp. 1371-1429, 2014. (ICCM 2019 Best Paper Award) [code]
  58. Minimax sparse logistic regression for very high dimensional feature selection (with Mingkui Tan and Ivor W. Tsang). IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Vol. 24(10), pp. 1609-1622, 2013.
  59. Generalized Multiple Kernel Learning with Data- Dependent Priors (with Qi Mao, Ivor W. Tsang and Shenghua Gao). IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 26(6):1134-1148, 2015.
  60. Convex matching pursuit for large-scale sparse coding and subset selections (with Mingkui Tan, Ivor W. Tsang and Xinming Zhang). Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), Toronto, Canada, 2012.
  61. Learning sparse SVM for feature selection on very high dimensional datasets (with Mingkui Tan and Ivor W. Tsang). Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010. [code]
  62. Riemannian Pursuit for Big Matrix Recovery(with Mingkui Tan, Ivor W. Tsang, Bart Vandereycken, Sinno J. Pan). Proceedings of the 30th International Conference on Machine Learning (ICML), Beijing, 2014. [code]
  63. Dimensionality Reduction via Graph Structure Learning. The 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’15), Sydney, Australia, 2015. [codes in MATLAB and R]
  64. Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data. Proceedings of the 30th AAAI Conference on Artificial Intelligence. (With Mingkui Tan, Ivor W.Tsang, Anton Van Den Hengel, Qinfeng Shi et.al), 2016.
  65. Theoretical and computational aspects of entanglement (with professors Harm Derksen, Shmuel Friedland and Lek-Heng Lim). 2017. ([ arxiv ])
  66. Progression of chronic kidney disease in African American with type 2 diabetes mellitus using topology learning in electronic medical records (with Jing Su et al.). preprint, 2018. [biorxiv]