research-article

Subspace Clustering by Relaxed Block Diagonal Representation

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Xiangchu FengAuthors Info & Claims

ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

Pages 343 - 348

https://doi.org/10.1145/3364836.3364905

Published: 24 August 2019 Publication History

Abstract

The deluge of high dimensional data brings great challenges to data analysis, processing and storage. Subspace clustering aims to solve this dilemma by uncovering the latent low-dimensional structure inherent in high dimensional data. The most popular methods are self-representation (SR) based methods, which learn an affinity matrix by using the SR of the dataset and then apply the spectral clustering to obtain the final clustering results. The SR basically determines the clustering performance; therefore, existing methods use various regularity to impose clustering advantageous structure on the SR. In this work, we incorporate an orthogonal matrix in the block diagonal regularity (BDR) and adapt the BDR model as the relaxed BDR(RBDR) model. Our model enforces the block diagonal structure of SR, but allows an orthogonal matrix difference. We also present an alternative minimization algorithm to solve our model. Extended experimental results show that our model can greatly improve the performance of the BDR, especially when the dataset is not arranged cluster by cluster, which is true in practice. Therefore, our method fits better for practical data.

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Cited By

Du GZhou LLü KWu HXu Z(2023)Multiview Subspace Clustering With Multilevel Representations and Adversarial RegularizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316554234:12(10279-10293)Online publication date: Dec-2023
https://doi.org/10.1109/TNNLS.2022.3165542

Index Terms

Subspace Clustering by Relaxed Block Diagonal Representation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

August 2019

370 pages

ISBN:9781450372626

DOI:10.1145/3364836

Copyright © 2019 ACM.

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Published: 24 August 2019

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ISICDM 2019

ISICDM 2019: The Third International Symposium on Image Computing and Digital Medicine

August 24 - 26, 2019

Xi'an, China

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Cited By

Du GZhou LLü KWu HXu Z(2023)Multiview Subspace Clustering With Multilevel Representations and Adversarial RegularizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.316554234:12(10279-10293)Online publication date: Dec-2023
https://doi.org/10.1109/TNNLS.2022.3165542

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