Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2696-2703.DOI: 10.11772/j.issn.1001-9081.2023091253
• Data science and technology • Previous Articles Next Articles
Shunyong LI1, Shiyi LI1,2, Rui XU1, Xingwang ZHAO2()
Received:
2023-09-12
Revised:
2023-10-31
Accepted:
2023-11-02
Online:
2023-11-23
Published:
2024-09-10
Contact:
Xingwang ZHAO
About author:
LI Shunyong, born in 1975, Ph. D., professor. His research interests include statistical machine learning, data mining.Supported by:
通讯作者:
赵兴旺
作者简介:
李顺勇(1975—),男,山西大同人,教授,博士,主要研究方向:统计机器学习、数据挖掘基金资助:
CLC Number:
Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion[J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
李顺勇, 李师毅, 胥瑞, 赵兴旺. 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》唯一官方网站, 2024, 44(9): 2696-2703.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091253
数据集 | 类别数 | 样本数 | 视图 | 维度 |
---|---|---|---|---|
Caltech101-20 | 20 | 2 386 | HOG | 1 984 |
GIST | 512 | |||
LandUse-21 | 21 | 2 100 | PHOG | 59 |
LBP | 40 | |||
Scene-15 | 15 | 4 485 | PHOG | 59 |
GIST | 20 | |||
Noisy-MNIST | 10 | 20 000 | 视 | 784 |
视 | 784 |
Tab. 1 Experimental datasets
数据集 | 类别数 | 样本数 | 视图 | 维度 |
---|---|---|---|---|
Caltech101-20 | 20 | 2 386 | HOG | 1 984 |
GIST | 512 | |||
LandUse-21 | 21 | 2 100 | PHOG | 59 |
LBP | 40 | |||
Scene-15 | 15 | 4 485 | PHOG | 59 |
GIST | 20 | |||
Noisy-MNIST | 10 | 20 000 | 视 | 784 |
视 | 784 |
算法 | Caltech101-20 | LandUse-21 | Scene-15 | Noisy-MNIST | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | |
AE2Nets | 33.61 | 49.20 | 24.99 | 19.22 | 23.03 | 5.75 | 27.88 | 31.35 | 13.93 | 38.67 | 33.79 | 19.99 |
PVC | 41.42 | 56.53 | 31.00 | 21.33 | 23.14 | 8.10 | 25.61 | 25.31 | 11.25 | 35.97 | 27.74 | 16.99 |
DAIMC | 44.63 | 59.53 | 32.70 | 19.30 | 19.45 | 5.80 | 23.60 | 21.88 | 9.44 | 34.44 | 27.15 | 16.42 |
EERIMVC | 40.66 | 51.38 | 27.91 | 22.14 | 25.18 | 9.10 | 33.10 | 32.11 | 15.91 | 54.97 | 44.91 | 35.94 |
DCCA | 38.59 | 52.51 | 29.81 | 14.08 | 20.02 | 3.38 | 31.83 | 33.19 | 14.93 | 61.82 | 60.55 | 37.71 |
CPM-GAN | 41.42 | 55.89 | 33.74 | 19.02 | 21.58 | 6.11 | 27.30 | 27.18 | 11.93 | — | — | — |
PIC | 57.53 | 64.32 | 45.22 | 23.60 | 26.52 | 9.45 | 38.70 | 37.98 | 21.16 | — | — | — |
COMPLETER | 68.44 | 67.39 | 75.44 | 22.16 | 27.00 | 10.39 | 39.50 | 42.35 | 23.51 | 80.01 | 75.23 | 70.66 |
IMVCSAF | 73.93 | 70.16 | 85.16 | 22.19 | 27.92 | 11.24 | 40.04 | 42.15 | 23.94 | 86.59 | 79.00 | 76.57 |
Tab. 2 Experimental comparison on data with 50% miss rate
算法 | Caltech101-20 | LandUse-21 | Scene-15 | Noisy-MNIST | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | |
AE2Nets | 33.61 | 49.20 | 24.99 | 19.22 | 23.03 | 5.75 | 27.88 | 31.35 | 13.93 | 38.67 | 33.79 | 19.99 |
PVC | 41.42 | 56.53 | 31.00 | 21.33 | 23.14 | 8.10 | 25.61 | 25.31 | 11.25 | 35.97 | 27.74 | 16.99 |
DAIMC | 44.63 | 59.53 | 32.70 | 19.30 | 19.45 | 5.80 | 23.60 | 21.88 | 9.44 | 34.44 | 27.15 | 16.42 |
EERIMVC | 40.66 | 51.38 | 27.91 | 22.14 | 25.18 | 9.10 | 33.10 | 32.11 | 15.91 | 54.97 | 44.91 | 35.94 |
DCCA | 38.59 | 52.51 | 29.81 | 14.08 | 20.02 | 3.38 | 31.83 | 33.19 | 14.93 | 61.82 | 60.55 | 37.71 |
CPM-GAN | 41.42 | 55.89 | 33.74 | 19.02 | 21.58 | 6.11 | 27.30 | 27.18 | 11.93 | — | — | — |
PIC | 57.53 | 64.32 | 45.22 | 23.60 | 26.52 | 9.45 | 38.70 | 37.98 | 21.16 | — | — | — |
COMPLETER | 68.44 | 67.39 | 75.44 | 22.16 | 27.00 | 10.39 | 39.50 | 42.35 | 23.51 | 80.01 | 75.23 | 70.66 |
IMVCSAF | 73.93 | 70.16 | 85.16 | 22.19 | 27.92 | 11.24 | 40.04 | 42.15 | 23.94 | 86.59 | 79.00 | 76.57 |
算法 | Caltech101-20 | LandUse-21 | Scene-15 | Noisy-MNIST | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | |
AE2Nets | 49.10 | 65.38 | 35.66 | 24.79 | 30.36 | 10.35 | 36.10 | 40.39 | 22.08 | 56.98 | 46.83 | 36.98 |
PVC | 44.91 | 62.13 | 35.77 | 25.22 | 30.45 | 11.72 | 30.83 | 31.05 | 14.98 | 41.94 | 33.90 | 22.93 |
DAIMC | 45.48 | 61.79 | 32.40 | 24.35 | 29.35 | 10.26 | 32.09 | 33.55 | 17.42 | 39.18 | 35.69 | 23.65 |
EERIMVC | 43.28 | 55.04 | 30.42 | 24.92 | 29.57 | 12.24 | 39.60 | 38.99 | 22.06 | 65.47 | 57.69 | 49.54 |
DCCA | 41.89 | 59.14 | 33.39 | 15.51 | 23.15 | 4.43 | 36.18 | 38.92 | 20.87 | 85.53 | 89.44 | 81.87 |
CPM-GAN | 43.18 | 62.00 | 34.57 | 22.34 | 29.18 | 9.49 | 30.87 | 31.54 | 15.27 | — | — | — |
PIC | 62.27 | 67.93 | 51.56 | 24.86 | 29.74 | 10.48 | 38.72 | 40.46 | 22.12 | — | — | — |
COMPLETER | 70.18 | 68.06 | 77.88 | 25.63 | 31.73 | 13.05 | 41.07 | 44.68 | 24.78 | 89.08 | 88.86 | 85.47 |
IMVCSAF | 77.07 | 72.80 | 89.55 | 26.82 | 33.64 | 14.05 | 41.26 | 45.36 | 25.86 | 92.42 | 87.40 | 84.06 |
Tab. 3 Experimental comparison on complete data
算法 | Caltech101-20 | LandUse-21 | Scene-15 | Noisy-MNIST | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | ACC | NMI | ARI | |
AE2Nets | 49.10 | 65.38 | 35.66 | 24.79 | 30.36 | 10.35 | 36.10 | 40.39 | 22.08 | 56.98 | 46.83 | 36.98 |
PVC | 44.91 | 62.13 | 35.77 | 25.22 | 30.45 | 11.72 | 30.83 | 31.05 | 14.98 | 41.94 | 33.90 | 22.93 |
DAIMC | 45.48 | 61.79 | 32.40 | 24.35 | 29.35 | 10.26 | 32.09 | 33.55 | 17.42 | 39.18 | 35.69 | 23.65 |
EERIMVC | 43.28 | 55.04 | 30.42 | 24.92 | 29.57 | 12.24 | 39.60 | 38.99 | 22.06 | 65.47 | 57.69 | 49.54 |
DCCA | 41.89 | 59.14 | 33.39 | 15.51 | 23.15 | 4.43 | 36.18 | 38.92 | 20.87 | 85.53 | 89.44 | 81.87 |
CPM-GAN | 43.18 | 62.00 | 34.57 | 22.34 | 29.18 | 9.49 | 30.87 | 31.54 | 15.27 | — | — | — |
PIC | 62.27 | 67.93 | 51.56 | 24.86 | 29.74 | 10.48 | 38.72 | 40.46 | 22.12 | — | — | — |
COMPLETER | 70.18 | 68.06 | 77.88 | 25.63 | 31.73 | 13.05 | 41.07 | 44.68 | 24.78 | 89.08 | 88.86 | 85.47 |
IMVCSAF | 77.07 | 72.80 | 89.55 | 26.82 | 33.64 | 14.05 | 41.26 | 45.36 | 25.86 | 92.42 | 87.40 | 84.06 |
ACC | NMI | ARI | |||
---|---|---|---|---|---|
√ | 58.62 | 63.21 | 58.73 | ||
√ | 32.07 | 36.69 | 19.30 | ||
√ | 43.11 | 32.66 | 31.59 | ||
√ | √ | 52.65 | 57.94 | 45.80 | |
√ | √ | 60.48 | 63.15 | 62.42 | |
√ | √ | 66.55 | 68.67 | 70.87 | |
√ | √ | √ | 73.93 | 70.16 | 85.16 |
Tab. 4 Ablation experimental results
ACC | NMI | ARI | |||
---|---|---|---|---|---|
√ | 58.62 | 63.21 | 58.73 | ||
√ | 32.07 | 36.69 | 19.30 | ||
√ | 43.11 | 32.66 | 31.59 | ||
√ | √ | 52.65 | 57.94 | 45.80 | |
√ | √ | 60.48 | 63.15 | 62.42 | |
√ | √ | 66.55 | 68.67 | 70.87 | |
√ | √ | √ | 73.93 | 70.16 | 85.16 |
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