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133 results on '"You, Jiali"'

10. One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering

Author

You, Jiali, Ren, Zhenwen, Yu, F. Richard, and You, Xiaojian

Abstract

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a “three-stage” scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the “one-stage” scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to $O(n)$ and $O(n^{2})$ , respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Published
2024
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17. Explicit Local Coupling Global Structure Clustering

Author

Li, Haoran, Guo, Yulan, Ren, Zhenwen, Yu, F. Richard, You, Jiali, and You, Xiaojian

Abstract

Graph-based clustering has become an active topic due to the efficiency in characterizing the relationships between the samples via graph. To improve the quality of graph, recent works propose to utilize global and local information. However, existing methods may lead to a degenerated graph when facing noisy and uneven distributed data. Since 1) they preserve the local information by referring the similarity between each sample-pair, whose confidence is easily disturbed by the poor quality samples; and 2) although the global information is relatively robust to the noisy, existing methods have island effect that lies between local and global structures learning, such that the information of both can not be utilized mutually. To alleviate these issues, this paper presents explicit local coupling global structure clustering (ELGSC) to explicitly learn the local structure and global structure information via a coupling scheme. To be specific, we learn $l(\ll n)$ pseudo samples as the anchors to reflect local hot spots distribution, where $n$ is the number of samples. By referring the relationship between each anchor-sample pair, ELGSC is capable of obtaining an effective local bipartite graph to capture the local structure. Meanwhile, the self-expressiveness learning is adopted to pursue a lower-rank global affinity graph. Finally, a higher-order coupling learning framework is proposed to couple the learning of global affinity graph and local bipartite graph. Thus, local and global structure information could be propagated each other on both graphs. The experimental results on real datasets demonstrate the efficacy of the proposed method over state-of-the-arts.

Published
2023
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32. The BMP2 Signaling Axis Promotes Invasive Differentiation of Human Trophoblasts

Author

You, Jiali, primary, Wang, Wei, additional, Chang, Hsun-Ming, additional, Yi, Yuyin, additional, Zhao, Hongjin, additional, Zhu, Hua, additional, Sun, Yu, additional, Tang, Minyue, additional, Wang, Chunyan, additional, Sang, Yimiao, additional, Feng, Guofang, additional, Cheng, Shaobing, additional, Leung, Peter C. K., additional, and Zhu, Yi-Min, additional

Published
2021
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