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Joint Deep Multi-View Learning for Image Clustering.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Nov2021, Vol. 33 Issue 11, p3594-3606, 13p
- Publication Year :
- 2021
-
Abstract
- In this paper, a novel Deep Multi-view Joint Clustering (DMJC) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned simultaneously. Through the joint learning strategy, the clustering-friendly multi-view features and useful multi-view complementary information can be exploited effectively to improve the clustering performance. Under the proposed joint learning framework, we design two ingenious variants of deep multi-view joint clustering models, whose multi-view fusion is implemented by two kinds of simple yet effective schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence objective. Experiments on eight challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multi-view clustering methods, which proves the effectiveness of the proposed DMJC framework. To the best of our knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
LEARNING strategies
MACHINE learning
FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 33
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 153711849
- Full Text :
- https://doi.org/10.1109/TKDE.2020.2973981