Anchor Graph Based Low-Rank Incomplete Multi-View Subspace Clustering

doi:10.12141/j.issn.1000-565X.220069

Abstract

Abstract:

Traditional multi-view clustering task is for complete data. However, in practical tasks, due to the limitation of the information acquisition method, some views tend to contain missing data, and this leads to the problem of incomplete multi-view clustering. In view of this problem, most of the existing clustering models are based on non-negative matrix factorization or distance graph, and their co-optimization strategy can easily make the performance of the solution insecure and the global structure can’t be fully characterized. In order to improve the performance of clustering graph, this paper proposed an incomplete multi-view clustering algorithm ALIMSC based on low-rank subspace clustering and anchor graph. The algorithm first obtained the benchmark similarity matrix of data by incomplete multi-view subspace clustering algorithm APMC based on anchor graph, which was embedded in the low-rank subspace clustering model. The similarity matrix was obtained by dimensionality ascending alignment and weighted fusion, and the final clustering graph was obtained by making the similarity matrix as consistent as possible with the benchmark similarity matrix. ALIMSC algorithm characterized the low-dimensional subspace distribution of high-dimensional data by imposing rank minimization constraint on the similarity matrix of each view and emphasized the subspace structure of the data on the basis of the original anchor graph, that is, the block diagonality reflected in the cluster graph. Experimental results on several public datasets show that the proposed algorithm outperforms the classical incomplete multi-view algorithms.

Key words: clustering algorithm, low-rank representation, incomplete multi-view clustering, subspace clustering

CLC Number:

TP391

LIU Xiaolan, SHI Zongyu, YE Zehui, et al. Anchor Graph Based Low-Rank Incomplete Multi-View Subspace Clustering[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 60-70.

Figures/Tables 10

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数据集	样本数	视图数	类别数	样本维度
3Source	416	3	6	3 068，3 560，3 632
USPS-MNIST	500	2	10	784，256
UCI Digit	2 000	2	10	216，76
BBCSports	544	2	5	3 183，3 203
ORL	400	3	40	512，59，864
WebKB	203	3	4	1 703，230，230

数据集	算法	ACC					NMI
数据集	算法	r_PD=0.1	r_PD=0.3	r_PD=0.5	r_PD=0.7	r_PD=0.9	r_PD=0.1	r_PD=0.3	r_PD=0.5	r_PD=0.7	r_PD=0.9
USPS-MNIST	PVC	0.502	0.468	0.432	0.444	0.353	0.400	0.392	0.349	0.337	0.274
	MIC	0.832	0.810	0.734	0.690	0.610	0.759	0.695	0.615	0.574	0.569
	DAIMC	0.834	0.795	0.670	0.576	0.457	0.761	0.681	0.556	0.493	0.390
	IMSC_AGL	0.964	0.934	0.854	0.770	0.739	0.934	0.892	0.776	0.682	0.624
	OPIMC	0.580	0.502	0.486	0.438	0.354	0.603	0.487	0.461	0.422	0.293
	HCP-IMSC	0.949	0.938	0.887	0.810	0.662	0.900	0.881	0.800	0.734	0.576
	APMC	0.964	0.936	0.846	0.810	0.574	0.926	0.881	0.741	0.707	0.466
	ALIMSC	0.975	0.968	0.896	0.868	0.678	0.950	0.940	0.810	0.790	0.539
UCI Digit	PVC	0.605	0.537	0.566	0.522	0.554	0.580	0.531	0.544	0.482	0.510
	MIC	0.835	0.795	0.653	0.535	0.412	0.728	0.690	0.577	0.522	0.418
	DAIMC	0.739	0.674	0.566	0.491	0.453	0.647	0.589	0.530	0.469	0.429
	IMSC_AGL	0.891	0.890	0.899	0.849	0.731	0.819	0.813	0.819	0.757	0.663
	OPIMC	0.350	0.223	0.219	0.209	0.295	0.465	0.285	0.139	0.164	0.313
	HCP-IMSC	0.795	0.861	0.772	0.771	0.701	0.745	0.772	0.725	0.702	0.611
	APMC	0.817	0.814	0.811	0.826	0.794	0.832	0.820	0.814	0.714	0.682
	ALIMSC	0.921	0.915	0.902	0.856	0.828	0.874	0.849	0.831	0.763	0.726

算法	ACC				NMI
算法	BBC-Guardian	BBC-Reuters	Guardian-Reuters	3-Views	BBC-Guardian	BBC-Reuters	Guardian-Reuters	3-Views
PVC	0.662	0.578	0.001		0.569	0.569	0.642
MIC	0.731	0.772	0.654	0.817	0.551	0.639	0.538	0.675
DAIMC	0.540	0.564	0.512	0.596	0.426	0.448	0.381	0.473
IMSC_AGL	0.870	0.853	0.741	0.796	0.712	0.712	0.562	0.685
OPIMC	0.531	0.529	0.457	0.671	0.318	0.343	0.294	0.521
HCP-IMSC	0.818	0.877	0.813	0.899	0.654	0.743	0.614	0.781
APMC	0.837	0.845	0.849	0.851	0.680	0.687	0.706	0.702
ALIMSC	0.871	0.894	0.872	0.899	0.734	0.759	0.721	0.767

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