Li Liang

Li Liang(李亮) is a Ph.D. student at National University of Defence Technology (NUDT), Hunan, China since 2021. He is supervised by Prof. Keqin Li and Prof. Xinwang Liu. Before that, he received the Master degree from NUDT in 2020, and got the Bachelor degree in 2018 from Huazhong University of Science and Technology (HUST), Wuhan, China. His research interests include machine learning, bioinformatics, and self-supervised learning. Any discussions or concerns are welcomed! Please contact me via e-mail:liliang1037@gmail.com

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• [2024.03] I serve as a reviewer for IEEE TPAMI.
• [2024.01] One paper has been accepted by IEEE TKDE.
• [2023.09] I am visiting A*STAR for 18 months.
• [2023.07] I was awarded CSC Scholarship.
• [2023.04] One paper has been accepted by IEEE TNNLS.
• [2023.04] One paper has been accepted by IEEE TKDE.
• [2022.11] Two papers have been accepted by AAAI 2023.
• [2022.10] I won the China National Scholarship (top 0.2%).
• [2022.06] One paper has been accepted by ACMMM 2022.
• [2022.06] One paper has been accepted by IEEE TNNLS.
• [2021.12] I won the first prize of postgraduate academic scholarship at NUDT.
• [2020.10] I won the China National Scholarship (top 0.2%).
• [2020.10] I won the first prize of postgraduate academic scholarship at NUDT.

Representative papers are highlighted.

We propose a generalized and elegant denoising framework for multi-view bipartite graph clustering.

We propose a novel Jet-BGC model that integrating embedding learning and Structural Fusion.

We rethink existing paradigms and find that a common design is to construct the bipartite graph directly from the input data, i.e. only consider the unidirectional "encoding" process. Inspired by the popular "encoding-decoding" design in deep learning, we transfer it into graph machine learning and propose a novel model.

One crucial finding is that the existence of noisy features will incur "anchor shift", which deviates from the potential centroids. We propose a novel noisy feature filter mechanism to remedy the anchor shift, and we theoretically analyze the bounds of the bipartite graph's sparsity.

We mathematically disassemble the noise within kernel partition into dual noise, namely, Null space noise (N-noise) and Column space noise (C-noise), and propose an elegant method to minimize them. We observe that dual noise will pollute the block diagonal structures. An interesting finding is that C-noise exhibits stronger destruction than N-noise.

We investigate an important issue that how to localize the kernel matrix in multi-kernel clustering. Compared to the traditional KNN manner that neglects the ranking relationship of neighbors, this paper proposes a novel localized MKC algorithm coupled flexible graph learning, termd LSWMKC, which achieves fully exploring the latent local manifold.

We propose an elegant diverse kernel partition fusion method to get the optimal partition.

We propose to replace the complicated and consuming graph data augmentations by designing the parameter un-shared siamese encoders and perurbing the node embeddings.

We propose Hard Sample Aware Network (HSAN) to mine both the hard positive samples and hard negative samples with a comprehensive similarity measure criterion and a general dynamic sample weighing strategy.

We propose to fuse diverse bipartite graphs across multiple views that can avoid tune the anchor number manually.