Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (12): 3784-3789.DOI: 10.11772/j.issn.1001-9081.2023121866
• Artificial intelligence • Previous Articles Next Articles
Chenghao YANG, Jie HU(), Hongjun WANG, Bo PENG
Received:
2024-01-08
Revised:
2024-03-14
Accepted:
2024-03-15
Online:
2024-03-22
Published:
2024-12-10
Contact:
Jie HU
About author:
YANG Chenghao, born in 1999, M. S. candidate. His research interests include deep learning, multi-view clustering.Supported by:
通讯作者:
胡节
作者简介:
杨成昊(1999—),男,四川成都人,硕士研究生,CCF会员,主要研究方向:深度学习、多视图聚类基金资助:
CLC Number:
Chenghao YANG, Jie HU, Hongjun WANG, Bo PENG. Incomplete multi-view clustering algorithm based on attention mechanism[J]. Journal of Computer Applications, 2024, 44(12): 3784-3789.
杨成昊, 胡节, 王红军, 彭博. 基于注意力机制的不完备多视图聚类算法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3784-3789.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023121866
符号 | 描述 |
---|---|
补全的数据 | |
簇数 | |
第 | |
训练后用于聚类的嵌入 | |
键的维度 | |
第 | |
第 | |
第 |
Tab. 1 Description of symbols
符号 | 描述 |
---|---|
补全的数据 | |
簇数 | |
第 | |
训练后用于聚类的嵌入 | |
键的维度 | |
第 | |
第 | |
第 |
数据集 | 类别数 | 样本数 | 视图数 |
---|---|---|---|
Caltech101-20[ | 20 | 2 386 | 6 |
MNIST-USPS[ | 10 | 5 000 | 2 |
BDGP[ | 5 | 2 500 | 2 |
Fashion[ | 10 | 10 000 | 3 |
Scene[ | 15 | 4 485 | 2 |
Tab. 2 Datasets used in experiments
数据集 | 类别数 | 样本数 | 视图数 |
---|---|---|---|
Caltech101-20[ | 20 | 2 386 | 6 |
MNIST-USPS[ | 10 | 5 000 | 2 |
BDGP[ | 5 | 2 500 | 2 |
Fashion[ | 10 | 10 000 | 3 |
Scene[ | 15 | 4 485 | 2 |
缺失 比例 | 算法 | Caltech101-20 | MNIST-USPS | BDGP | Fashion | Scene | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ACC | NMI | ACC | NMI | ACC | NMI | ACC | NMI | ||
10 | CDIMC-net | 55.68 | 52.24 | 52.23 | 61.45 | 80.47 | 70.08 | 51.00 | 62.52 | 37.68 | 40.72 |
MKKM-IK | 51.46 | 48.12 | 75.25 | 64.64 | 65.01 | 49.62 | 70.08 | 61.29 | 39.64 | 40.38 | |
EE-R-IMVC | 52.28 | 55.47 | 75.07 | 64.27 | 62.28 | 43.82 | 72.83 | 65.78 | 37.56 | 38.21 | |
COMPLETER | 66.67 | 63.33 | 96.87 | 93.94 | 40.91 | 33.19 | 78.63 | 82.23 | 46.35 | 49.52 | |
OS-LF-IMVC | 56.75 | 52.14 | 62.29 | 49.14 | 82.78 | 60.25 | 62.54 | 52.36 | 37.42 | 38.66 | |
IMVCSAF | 70.02 | 60.00 | 87.26 | 81.15 | — | — | — | — | — | — | |
DSIMVC | 64.92 | 56.56 | 98.88 | 96.91 | 98.40 | 94.67 | 89.60 | 84.47 | 42.15 | 44.62 | |
本文算法 | 77.70 | 67.62 | 96.62 | 95.24 | 96.82 | 90.41 | 92.45 | 86.26 | 43.72 | 45.60 | |
30 | CDIMC-net | 53.84 | 50.08 | 49.72 | 64.40 | 74.67 | 67.64 | 44.73 | 54.67 | 36.24 | 40.26 |
MKKM-IK | 49.85 | 47.54 | 64.44 | 52.01 | 59.80 | 35.22 | 59.96 | 50.52 | 38.45 | 39.96 | |
EE-R-IMVC | 49.32 | 51.26 | 58.86 | 49.47 | 59.36 | 31.79 | 63.32 | 57.28 | 34.25 | 35.48 | |
COMPLETER | 66.95 | 63.89 | 95.56 | 92.31 | 41.80 | 31.15 | 71.68 | 77.12 | 44.87 | 47.74 | |
OS-LF-IMVC | 55.28 | 49.56 | 46.58 | 33.98 | 74.34 | 48.27 | 50.10 | 38.74 | 37.10 | 38.34 | |
IMVCSAF | 70.33 | 60.83 | 86.12 | 78.21 | — | — | — | — | — | — | |
DSIMVC | 62.92 | 53.74 | 97.89 | 94.50 | 96.93 | 90.34 | 87.47 | 81.76 | 39.56 | 43.16 | |
本文算法 | 73.81 | 64.60 | 96.38 | 94.11 | 96.25 | 89.84 | 91.82 | 84.55 | 40.53 | 42.81 | |
50 | CDIMC-net | 47.56 | 42.25 | 47.97 | 56.62 | 67.71 | 54.51 | 42.10 | 44.85 | 32.85 | 38.47 |
MKKM-IK | 43.28 | 41.32 | 49.74 | 37.67 | 52.56 | 24.55 | 46.38 | 38.25 | 35.21 | 36.82 | |
EE-R-IMVC | 40.66 | 51.38 | 45.58 | 34.15 | 42.48 | 21.39 | 51.16 | 43.50 | 33.10 | 32.11 | |
COMPLETER | 68.44 | 67.39 | 93.66 | 90.51 | 41.54 | 32.62 | 70.76 | 74.76 | 39.50 | 42.35 | |
OS-LF-IMVC | 50.37 | 46.34 | 32.83 | 22.22 | 59.71 | 30.56 | 37.47 | 30.04 | 33.67 | 34.23 | |
IMVCSAF | 73.93 | 70.16 | 86.59 | 79.00 | — | — | — | — | 40.04 | 42.15 | |
DSIMVC | 55.47 | 47.15 | 96.78 | 91.98 | 95.29 | 86.11 | 83.79 | 77.82 | 38.26 | 40.78 | |
本文算法 | 71.54 | 67.48 | 95.64 | 92.87 | 95.55 | 86.66 | 88.67 | 81.21 | 39.55 | 41.47 | |
70 | CDIMC-net | 38.24 | 40.44 | 31.78 | 34.79 | 56.11 | 39.70 | 37.61 | 46.05 | 29.91 | 37.78 |
MKKM-IK | 36.58 | 37.76 | 35.70 | 24.68 | 46.84 | 14.58 | 29.84 | 20.64 | 29.40 | 34.75 | |
EE-R-IMVC | 36.45 | 46.52 | 28.02 | 16.97 | 34.85 | 11.87 | 20.24 | 14.61 | 30.24 | 31.84 | |
COMPLETER | 62.22 | 56.67 | 83.80 | 81.18 | 39.63 | 24.47 | 69.33 | 70.23 | 36.21 | 41.72 | |
OS-LF-IMVC | 46.55 | 43.86 | 23.70 | 13.96 | 45.34 | 18.54 | 27.67 | 19.98 | 31.36 | 33.18 | |
IMVCSAF | 65.56 | 53.26 | 77.64 | 86.57 | — | — | — | — | — | — | |
DSIMVC | 41.92 | 34.38 | 93.34 | 85.64 | 92.14 | 79.37 | 75.51 | 71.53 | 35.78 | 39.45 | |
本文算法 | 64.64 | 65.42 | 93.78 | 88.74 | 93.28 | 81.17 | 80.71 | 72.67 | 36.88 | 40.20 |
Tab. 3 Comparison of ACC and NMI in clustering results of different algorithms
缺失 比例 | 算法 | Caltech101-20 | MNIST-USPS | BDGP | Fashion | Scene | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ACC | NMI | ACC | NMI | ACC | NMI | ACC | NMI | ||
10 | CDIMC-net | 55.68 | 52.24 | 52.23 | 61.45 | 80.47 | 70.08 | 51.00 | 62.52 | 37.68 | 40.72 |
MKKM-IK | 51.46 | 48.12 | 75.25 | 64.64 | 65.01 | 49.62 | 70.08 | 61.29 | 39.64 | 40.38 | |
EE-R-IMVC | 52.28 | 55.47 | 75.07 | 64.27 | 62.28 | 43.82 | 72.83 | 65.78 | 37.56 | 38.21 | |
COMPLETER | 66.67 | 63.33 | 96.87 | 93.94 | 40.91 | 33.19 | 78.63 | 82.23 | 46.35 | 49.52 | |
OS-LF-IMVC | 56.75 | 52.14 | 62.29 | 49.14 | 82.78 | 60.25 | 62.54 | 52.36 | 37.42 | 38.66 | |
IMVCSAF | 70.02 | 60.00 | 87.26 | 81.15 | — | — | — | — | — | — | |
DSIMVC | 64.92 | 56.56 | 98.88 | 96.91 | 98.40 | 94.67 | 89.60 | 84.47 | 42.15 | 44.62 | |
本文算法 | 77.70 | 67.62 | 96.62 | 95.24 | 96.82 | 90.41 | 92.45 | 86.26 | 43.72 | 45.60 | |
30 | CDIMC-net | 53.84 | 50.08 | 49.72 | 64.40 | 74.67 | 67.64 | 44.73 | 54.67 | 36.24 | 40.26 |
MKKM-IK | 49.85 | 47.54 | 64.44 | 52.01 | 59.80 | 35.22 | 59.96 | 50.52 | 38.45 | 39.96 | |
EE-R-IMVC | 49.32 | 51.26 | 58.86 | 49.47 | 59.36 | 31.79 | 63.32 | 57.28 | 34.25 | 35.48 | |
COMPLETER | 66.95 | 63.89 | 95.56 | 92.31 | 41.80 | 31.15 | 71.68 | 77.12 | 44.87 | 47.74 | |
OS-LF-IMVC | 55.28 | 49.56 | 46.58 | 33.98 | 74.34 | 48.27 | 50.10 | 38.74 | 37.10 | 38.34 | |
IMVCSAF | 70.33 | 60.83 | 86.12 | 78.21 | — | — | — | — | — | — | |
DSIMVC | 62.92 | 53.74 | 97.89 | 94.50 | 96.93 | 90.34 | 87.47 | 81.76 | 39.56 | 43.16 | |
本文算法 | 73.81 | 64.60 | 96.38 | 94.11 | 96.25 | 89.84 | 91.82 | 84.55 | 40.53 | 42.81 | |
50 | CDIMC-net | 47.56 | 42.25 | 47.97 | 56.62 | 67.71 | 54.51 | 42.10 | 44.85 | 32.85 | 38.47 |
MKKM-IK | 43.28 | 41.32 | 49.74 | 37.67 | 52.56 | 24.55 | 46.38 | 38.25 | 35.21 | 36.82 | |
EE-R-IMVC | 40.66 | 51.38 | 45.58 | 34.15 | 42.48 | 21.39 | 51.16 | 43.50 | 33.10 | 32.11 | |
COMPLETER | 68.44 | 67.39 | 93.66 | 90.51 | 41.54 | 32.62 | 70.76 | 74.76 | 39.50 | 42.35 | |
OS-LF-IMVC | 50.37 | 46.34 | 32.83 | 22.22 | 59.71 | 30.56 | 37.47 | 30.04 | 33.67 | 34.23 | |
IMVCSAF | 73.93 | 70.16 | 86.59 | 79.00 | — | — | — | — | 40.04 | 42.15 | |
DSIMVC | 55.47 | 47.15 | 96.78 | 91.98 | 95.29 | 86.11 | 83.79 | 77.82 | 38.26 | 40.78 | |
本文算法 | 71.54 | 67.48 | 95.64 | 92.87 | 95.55 | 86.66 | 88.67 | 81.21 | 39.55 | 41.47 | |
70 | CDIMC-net | 38.24 | 40.44 | 31.78 | 34.79 | 56.11 | 39.70 | 37.61 | 46.05 | 29.91 | 37.78 |
MKKM-IK | 36.58 | 37.76 | 35.70 | 24.68 | 46.84 | 14.58 | 29.84 | 20.64 | 29.40 | 34.75 | |
EE-R-IMVC | 36.45 | 46.52 | 28.02 | 16.97 | 34.85 | 11.87 | 20.24 | 14.61 | 30.24 | 31.84 | |
COMPLETER | 62.22 | 56.67 | 83.80 | 81.18 | 39.63 | 24.47 | 69.33 | 70.23 | 36.21 | 41.72 | |
OS-LF-IMVC | 46.55 | 43.86 | 23.70 | 13.96 | 45.34 | 18.54 | 27.67 | 19.98 | 31.36 | 33.18 | |
IMVCSAF | 65.56 | 53.26 | 77.64 | 86.57 | — | — | — | — | — | — | |
DSIMVC | 41.92 | 34.38 | 93.34 | 85.64 | 92.14 | 79.37 | 75.51 | 71.53 | 35.78 | 39.45 | |
本文算法 | 64.64 | 65.42 | 93.78 | 88.74 | 93.28 | 81.17 | 80.71 | 72.67 | 36.88 | 40.20 |
KNN | Attention | Pearson | ACC | NMI |
---|---|---|---|---|
√ | 56.84 | 55.73 | ||
√ | 82.32 | 75.98 | ||
√ | 49.38 | 48.25 | ||
√ | √ | 82.41 | 76.29 | |
√ | √ | 88.12 | 79.48 | |
√ | √ | √ | 88.67 | 81.21 |
Tab. 4 Results of ablation experiments on Fashion dataset
KNN | Attention | Pearson | ACC | NMI |
---|---|---|---|---|
√ | 56.84 | 55.73 | ||
√ | 82.32 | 75.98 | ||
√ | 49.38 | 48.25 | ||
√ | √ | 82.41 | 76.29 | |
√ | √ | 88.12 | 79.48 | |
√ | √ | √ | 88.67 | 81.21 |
1 | 徐光生,王士同. 基于双重低秩分解的不完整多视图子空间学习[J]. 智能系统学报, 2022, 17(6): 1084-1092. |
XU G S, WANG S T. Incomplete multi-view subspace learning based on dual low-rank decomposition[J]. CAAI Transactions on Intelligent Systems, 2022, 17(6): 1084-1092. | |
2 | LIU X. Incomplete multiple kernel alignment maximization for clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(3): 1412-1424. |
3 | LIU X, LI M, TANG C, et al. Efficient and effective regularized incomplete multi-view clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(8): 2634-2646. |
4 | WEN J, SUN H, FEI L, et al. Consensus guided incomplete multi-view spectral clustering[J]. Neural Networks, 2021, 133: 207-219. |
5 | ZHANG Y, LIU X, WANG S, et al. One-stage incomplete multi-view clustering via late fusion [C]// Proceedings of the 29th ACM International Conference on Multimedia. New York: ACM, 2021: 2717-2725. |
6 | LIANG N, YANG Z, XIE S. Incomplete multi-view clustering with sample-level auto-weighted graph fusion [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(6): 6504-6511. |
7 | 崔金荣,黄诚. 改进的自步深度不完备多视图聚类[J]. 数据采集与处理, 2022, 37(5): 1036-1048. |
CUI J R, HUANG C. Improved self-paced deep incomplete multi-view clustering[J]. Journal of Data Acquisition and Processing, 2022, 37(5): 1036-1048. | |
8 | LIN Y, GOU Y, LIU X, et al. Dual contrastive prediction for incomplete multi-view representation learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 4447-4461. |
9 | WEN J, ZHANG Z, XU Y, et al. CDIMC-net: cognitive deep incomplete multi-view clustering network[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2020: 3230-3236. |
10 | LIN Y, GOU Y, LIU Z, et al. COMPLETER: incomplete multi-view clustering via contrastive prediction [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 11169-11178. |
11 | XU J, LI C, REN Y, et al. Deep incomplete multi-view clustering via mining cluster complementarity [C]// Proceedings of the 36th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2022: 8761-8769. |
12 | 李顺勇,李师毅,胥瑞,等.基于自注意力融合的不完整多视图聚类算法[J]. 计算机应用, 2024, 44(9):2696-2703. |
LI S Y, LI S Y, XU R, et al. Incomplete multi-view clustering algorithm based on self-attention fusion[J]. Journal of Computer Applications, 2024, 44(9):2696-2703. | |
13 | SEDGWICK P. Pearson's correlation coefficient [J]. BMJ, 2012, 345: No.e4483. |
14 | AHMED M, SERAJ R, ISLAM S M S. The k‑means algorithm: a comprehensive survey and performance evaluation[J]. Electronics, 2020, 9(8): No.1295. |
15 | UDDIN S, HAQUE I, LU H, et al. Comparative performance analysis of K-Nearest Neighbour (KNN) algorithm and its different variants for disease prediction [J]. Scientific Reports, 2022, 12: No.6256. |
16 | BRAUWERS G, FRASINCAR F. A general survey on attention mechanisms in deep learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 3279-3298. |
17 | FANG U, LI M, LI J, et al. A comprehensive survey on multi-view clustering [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12): 12350-12368. |
18 | LI Y, NIE F, HUANG H, et al. Large-scale multi-view spectral clustering via bipartite graph [C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2750-2756. |
19 | TZENG E, HOFFMAN J, SAENKO K, et al. Adversarial discriminative domain adaptation [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 2962-2971. |
20 | CAI X, WANG H, HUANG H, et al. Joint stage recognition and anatomical annotation of drosophila gene expression patterns [J]. Bioinformatics, 2012, 28(12): i16-i24. |
21 | ZOU X, KONG X, WONG W, et al. FashionAI: a hierarchical dataset for fashion understanding [C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2019: 296-304. |
22 | ZHOU B, ZHAO H, PUIG X, et al. Scene parsing through ADE20K dataset [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 5122-5130. |
23 | TANG H, LIU Y. Deep safe incomplete multi-view clustering: theorem and algorithm [C]// Proceedings of the 39th International Conference on Machine Learning. New York: JMLR.org, 2022: 21090-21110. |
24 | VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605. |
25 | LI Y F, ZHOU Z H. Towards making unlabeled data never hurt[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 175-188. |
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