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Automatically weighted binary multi-view clustering via deep initialization (AW-BMVC)
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DOI：10.1016/j.patcog.2022.109281.png)
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Abstract
En 中文
Clustering is inherently a process of exploratory data analysis. It has attracted more attention recently because much real-world data consists of multiple representations or views. However, it becomes in-creasingly problematic when dealing with large and heterogeneous data. It is worth noting that sev-eral approaches have been developed to increase computational efficiency, although most of them have some drawbacks: (1) Most existing techniques consider equal or static weights to quantify importance across different views and samples, so common and complementary features cannot be used. (2) The clustering task is performed by arbitrary initialization without caring about the rich structure of the joint discrete representation, and thus poorly executed. In this paper, we propose a novel approach called Auto-Weighted Binary Multi-View Clustering Via Deep Initialization for large-scale multi-view cluster-ing based on two main scenarios. First, we consider the distinction between different views based on the importance of samples, and therefore apply a dynamic learning strategy for the automatic weighting of views and samples. Second, in the context of initializing binary clustering, we develop a new CNN feature and use a low-dimensional binary embedding by exploiting the efficient capabilities of Fourier mapping. Moreover, our approach simultaneously learns a joint discrete representation and performs direct clus-tering using a constrained binary matrix factorization; the optimization problem is perfectly solved in a unified learning model. Experimental results conducted on several challenging datasets demonstrate the effectiveness and superiority of the proposed approach over state-of-the-art methods in terms of accu-racy, normalized mutual information, and purity.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keywords:
Multi -view clustering
Large scale
Anchors
Discrete representation and BD-FFT
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