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Deep multi-view subspace clustering via structure-preserved multi-scale features fusion

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Abstract

Multi-view subspace clustering has received widespread attention. Since data may violate the linear assumption in many practical applications, multi-view subspace clustering methods based on deep neural network are developed in recent years. However, most existing DNN based methods only focus on the utilization of the deepest features (i.e., features extracted from the deepest layer), regardless of the informative shallow features (i.e., features extracted from the shallow ones) and the fusion of multi-scale features (i.e., deep and shallow features), which may make their clustering performance suboptimal. In addition, these methods usually do not impose appropriate constraints on the extracted features to ensure that they can maintain the inherent local geometric structure of multi-view data, which is unfavorable for subspace learning. To deal with the limitations mentioned above, we propose a novel DNN based subspace clustering method for multi-view data called Deep Multi-view Subspace Clustering via Structure-preserved Multi-scale Features Fusion. Specifically, the graph knowledge of multi-view data is introduced to constrain the extracted features so that they have the structure-preserved property. Simultaneously, by fully exploiting the consistency of the extracted structure-preserved multi-scale features, DMSC-SMFF can learn the more discriminative common shared subspace representation, which can be employed by the spectral clustering module to obtain clustering results. Moreover, the optimization algorithm based on the Alternating Direction Method (ADM) is developed to optimize our objective function. Furthermore, DMSC-SMFF can also be applied to single-view data. Plentiful experimental results on five benchmark datasets demonstrate the effectiveness and superiority of the proposed method.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://mlg.ucd.ie/datasets/3sources.html.

  2. http://mlg.ucd.ie/datasets/segment.html.

  3. http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html.

  4. http://archive.ics.uci.edu/ml/datasets/multiple+features.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China [Grant Numbers 62076115, 61976041]; the National Key R &D Program of China [Grant Number 2018AAA0100300]; the LiaoNing Revitalization Talents Program [Grant Number XLYC1907169]; and the Program of Star of Dalian Youth Science and Technology [Grant Numbers 2019RQ033, 2020RQ053].

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Kaiqiang Xu & Zhixun Su

  2. School of Mathematics, Liaoning Normal University, Dalian, 116029, Liaoning, China

    Kewei Tang

  3. Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Kaiqiang Xu & Zhixun Su

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  1. Kaiqiang Xu
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Correspondence to Kewei Tang.

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Xu, K., Tang, K. & Su, Z. Deep multi-view subspace clustering via structure-preserved multi-scale features fusion. Neural Comput & Applic 35, 3203–3219 (2023). https://doi.org/10.1007/s00521-022-07864-4

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