Abstract
Multi-view clustering is a challenging task due to the distinct feature distributions among different views. To permit complementarity while exploiting consistency among views, some multi-layer models have been developed. These models usually enforce consistent representation on the top layer for clustering purpose while allowing the other layers to represent various attributes of the multi-view data. However, a single consistent layer is often insufficient especially for complicated real-world tasks. In addition, the existing models often represent different views using the same number of layers without taking the various levels of complexity of different views into account. Furthermore, different views are often considered to be equal in the clustering process, which does not necessarily hold in many applications. To address these issues, in this paper, we present a hierarchical ensemble framework for multi-view clustering (HEMVC). It is superior to the existing methods in three facets. Firstly, HEMVC allows for different views to share more than one consistent layers and implement ensemble clustering on all shared layers. Secondly, it facilitates an adaptive clustering scheme by automatically quantifying the contribution of each layer and each view in the ensemble learning process. Thirdly, it represents different views using different numbers of layers to compensate various complexities of different views. To realize HEMVC, a two-stage algorithm has been derived and implemented. The experimental results on five benchmark datasets illustrate its performance by comparing with the state-of-the-art methods.
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Notes
- 1.
To unify the number of the top shared layers in different views, the layer is numbered from top to bottom, which is opposite with Deep Semi-NMF [17]. It does not affect the rigorousness.
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Acknowledgements
This work was supported in part by Beijing Natural Science Foundation under Grant Z180006.
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Gao, F., Yang, L. (2021). Hierarchical Ensemble for Multi-view Clustering. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_23
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