[1]WANG Yingbo,GUO Kaixue.Incomplete multiview clustering based on view mapping and cyclic consistency generation[J].CAAI Transactions on Intelligent Systems,2025,20(2):316-328.[doi:10.11992/tis.202311044]
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Incomplete multiview clustering based on view mapping and cyclic consistency generation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 316-328 Column: 学术论文—机器学习 Public date: 2025-03-05
Title:
Incomplete multiview clustering based on view mapping and cyclic consistency generation
Author(s):
WANG Yingbo;  GUO Kaixue
Software College, Liaoning Technical University, Huludao 125105, China
Keywords:
data mining; clustering; multiview learning; incomplete multiview clustering; deep learning; autoencoder; generate adversarial networks; KL-divergence
CLC:
TP391
DOI:
10.11992/tis.202311044
Abstract:
Traditional clustering assumes that each view is complete without accounting for incomplete views caused by data corruption, device failures, and other factors. To address this issue, most existing methods rely on kernel and nonnegative matrix factorization without explicitly compensating for data loss in each view, and the potential representation of learning does not fully account for clustering tasks. An incomplete multiview clustering method (MG-IMC) with view mapping and cyclic consistency generation is designed to address the aforementioned limitation. This method leverages existing data information to generate missing data for each view through a single generative adversarial network, using shared potential representations provided by other views. Weighted adaptive fusion is applied to capture enhanced generic structures on the generated complete dataset, followed by clustering based on KL-divergence loss. The joint training of encoding common representations and generating missing data allows the model to recover missing data while simultaneously generating clustering-friendly common representations. Experiment results demonstrate that this algorithm outperforms existing methods in clustering performance.
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Last Update: 2025-03-05

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