A journal of IEEE and CAA , publishes
high-quality papers in English on original
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Volume 11
Issue 4
IEEE/CAA Journal of Automatica Sinica
| Citation: | X. Wu, Z. Ren, and F. Yu, “Parameter-free shifted Laplacian reconstruction for multiple kernel clustering,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 1072–1074, Apr. 2024. doi: 10.1109/JAS.2023.123600
|
| [1] |
Y. Wang, Z. Zhang, and Y. Lin, “Multi-cluster feature selection based on isometric mapping,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 570–572, Mar. 2022. doi: 10.1109/JAS.2021.1004398
|
| [2] |
L. Zhang, S. Feng, G. Duan, Y. Li, and G. Liu, “Detection of microaneurysms in fundus images based on an attention mechanism,” Genes, vol. 10, no. 10, p. 817, Oct. 2019. doi: 10.3390/genes10100817
|
| [3] |
S. Wang, X. Lin, Z. Fang, S. Du, and G. Xiao, “Contrastive consensus graph learning for multi-view clustering,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2027–2030, Nov. 2022. doi: 10.1109/JAS.2022.105959
|
| [4] |
R. Wang, J. Lu, Y. Lu, F. Nie, and X. Li, “Discrete and parameter-free multiple kernel k-means,” IEEE Trans. Image Processing, vol. 31, pp. 2796–2808, Mar. 2022. doi: 10.1109/TIP.2022.3141612
|
| [5] |
Z. Ren, S. Yang, Q. Sun, and T. Wang, “Consensus affinity graph learning for multiple kernel clustering,” IEEE Trans. Cybern., vol. 51, no. 6, pp. 3273–3284, Jun. 2021. doi: 10.1109/TCYB.2020.3000947
|
| [6] |
S. Zhou, Q. Ou, X. Liu, S. Wang, L. Liu, S. Wang, E. Zhu, J. Yin, and X. Xu, “Multiple kernel clustering with compressed subspace alignment,” IEEE Trans. Neural Networks and Learning Syst., vol. 34, no. 1, pp. 252–263, Jan. 2023. doi: 10.1109/TNNLS.2021.3093426
|
| [7] |
S. Zhou, X. Liu, J. Liu, X. Guo, Y. Zhao, E. Zhu, Y. Zhai, J. Yin, and W. Gao, “Multi-view spectral clustering with optimal neighborhood Laplacian matrix,” in Proc. AAAI Conf. Artificial Intelligence, 2020, vol. 34, pp. 6965–6972.
|
| [8] |
J. Zhang, L. Li, S. Wang, J. Liu, Y. Liu, X. Liu, and E. Zhu, “Multiple kernel clustering with dual noise minimization,” in Proc. 30th ACM Int. Conf. Multimedia, 2022, pp. 3440–3450.
|
| [9] |
Y. Li, P. Hu, Z. Liu, D. Peng, J. Zhou, and X. Peng, “Contrastive clustering,” in Proc. AAAI Conf. Artificial Intelligence, 2021, vol. 35, pp. 8547–8555.
|
| [10] |
H. Wang, Y. Yang, B. Liu, and H. Fujita, “A study of graph-based system for multi-view clustering,” Knowl.-Based Systems, vol. 163, pp. 1009–1019, Jan. 2019. doi: 10.1016/j.knosys.2018.10.022
|
| [11] |
A. Khan and P. Maji, “Approximate graph Laplacians for multimodal data clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 43, no. 3, pp. 798–813, Mar. 2021. doi: 10.1109/TPAMI.2019.2945574
|
| [12] |
S. Yu, X. H. Liu, L.-C. Tranchevent, W. Glänzel, J. AK. Suykens, B. De Moor, and Y. Moreau, “Optimized data fusion for K-means Laplacian clustering,” Bioinformatics, vol. 27, no. 1, pp. 118–126, Jan. 2011. doi: 10.1093/bioinformatics/btq569
|
| [13] |
J. Dattorro, Convex Optimization & Euclidean Distance Geometry. Palo Alto, USA: Meboo, 2005.
|
| [14] |
J. You, Z. Ren, Q. Sun, Y. Sun, and X. Li, “Approximate shifted Laplacian reconstruction for multiple kernel clustering,” in Proc. 30th ACM Int. Conf. Multimedia, 2022, pp. 2862–2870.
|
| [15] |
L. Du, P. Zhou, L. Shi, H. Wang, M. Fan, W. Wang, and Y.-D. Shen, “Robust multiple kernel k-means using l2; 1-norm,” in Proc. 24th Int. Conf. Artificial Intelligence, 2015, pp. 3476–3482.
|
| [16] |
F. Nie, G. Cai, and X. Li, “Multi-view clustering and semi-supervised classification with adaptive neighbours,” in Proc. 31st AAAI Conf. Artificial Intelligence, 2017, vol. 31, pp. 2408–2414.
|
| [17] |
M. Chen, L. Huang, C. Wang, and D. Huang, “Multi-view clustering in latent embedding space,” in Proc. AAAI Conf. Artificial Intelligence, 2020, vol. 34, pp. 3513–3520.
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