[1]马睿,周治平.结合地标点与自编码的快速多视图聚类网络[J].智能系统学报,2022,17(2):333-340.[doi:10.11992/tis.202101011]
MA Rui,ZHOU Zhiping.Fast multiview clustering network combining landmark points and autoencoder[J].CAAI Transactions on Intelligent Systems,2022,17(2):333-340.[doi:10.11992/tis.202101011]
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MA Rui,ZHOU Zhiping.Fast multiview clustering network combining landmark points and autoencoder[J].CAAI Transactions on Intelligent Systems,2022,17(2):333-340.[doi:10.11992/tis.202101011]
结合地标点与自编码的快速多视图聚类网络
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第2期
页码:
333-340
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-03-05
- Title:
- Fast multiview clustering network combining landmark points and autoencoder
- Keywords:
- multiview clustering; landmark point clustering; weighted PageRank; autoencoder; eigendecomposition; joint learning; cluster analysis; data mining
- 分类号:
- TP181
- 摘要:
- 针对目前存在的多视图聚类方法大多是对聚类准确性进行研究而未着重于提升算法效率，从而难以应用于大规模数据的现象，本文提出一种结合地标点和自编码的快速多视图聚类算法。利用加权$ \mathrm{P}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{R}\mathrm{a}\mathrm{n}\mathrm{k} $排序算法选出每个视图中最具代表性的地标点。使用凸二次规划函数从数据中直接生成多个视图的相似度矩阵，求得多个视图的共识相似度矩阵以有效利用多个视图包含的具有一致性和互补性的聚类有效信息，将获得的具有低存储开销性能的共识相似度矩阵输入自编码器替代拉普拉斯矩阵特征分解，在联合学习框架下同时更新自编码器参数和聚类中心从而在降低计算复杂度的同时保证聚类精度。在5个多视图数据集上的实验证明了本文算法相对于其他多视图算法在运行时间上的优越性。
- Abstract:
- Currently, most existing multiview clustering methods only focus on the accuracy of clustering and pay little attention to the improvement of the efficiency of the algorithm, which makes it difficult to apply them to large-scale datasets. This paper proposes a fast multiview clustering algorithm combining landmarks and autoencoder. The weighted PageRank algorithm is adopted to select the most representative landmark points in each view. The similarity matrix of multiple views is directly generated through the convex quadratic programming function. To effectively use the consistent and complementary clustering effective information contained in multiple views, the consensus similarity matrix of multiple views is obtained. The obtained consensus similarity matrix with low storage overhead performance is inputted to the autoencoder to replace the Laplacian matrix eigendecomposition. The proposed algorithm updates the autoencoder parameters and clustering centers under the framework of joint learning to ensure clustering accuracy while reducing computational complexity. Experiments in five multiview datasets show that the proposed algorithm is better than other multiview algorithms in terms of running time.
- 参考文献/References:
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- 相似文献/References:
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[1]储德润,周治平.加权PageRank改进地标表示的自编码谱聚类算法[J].智能系统学报,2020,15(2):302.[doi:10.11992/tis.201904021]
CHU Derun,ZHOU Zhiping.An autoencoder spectral clustering algorithm for improving landmark representation by weighted PageRank[J].CAAI Transactions on Intelligent Systems,2020,15():302.[doi:10.11992/tis.201904021]
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WANG Lijuan,DING Shifei,XIA Jing.Multiview low-rank sparse subspace clustering based on diversity[J].CAAI Transactions on Intelligent Systems,2023,18():399.[doi:10.11992/tis.202110026]
备注/Memo
收稿日期:2021-01-08。
作者简介:马睿，硕士研究生，主要研究方向为多视图聚类;周治平，教授，主要研究方向为智能检测、自动化装置、网络安全。发表学术论文80余篇
通讯作者:周治平.E-mail:zzp@jiangnan.edu.cn
更新日期/Last Update:
1900-01-01