Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 32, Issue 1, Pages 443-449Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.2978389
Keywords
Kernel; Adaptive systems; Clustering methods; Clustering algorithms; Sparse matrices; Learning systems; Neural networks; Adaptive neighbors; autoencoder; deep clustering; parameter-free similarity; structured graph
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Funding
- National Natural Science Foundation of China [61871470, U1801262, U1864204, 61773316]
- China Postdoctoral Science Foundation [2018M643765, 2019T120960]
- Xi'an Postdoctoral Innovation Base Funding
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This paper introduces a novel deep clustering method, ACC_AN, which combines structured graph learning with adaptive neighbors to explore the nonlinear structure of data and strengthen the correlations among deep representations during the learning process.
The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously.
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