[1]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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Fast multiview clustering network combining landmark points and autoencoder
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 2
Page number:
333-340
Column:
学术论文—机器感知与模式识别
Public date:
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
- CLC:
- TP181
- DOI:
- 10.11992/tis.202101011
- 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.
- References:
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