Autoencoder Constrained Clustering With Adaptive Neighbors

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 t...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 32; číslo 1; s. 443 - 449
Hlavní autoři: Li, Xuelong, Zhang, Rui, Wang, Qi, Zhang, Hongyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive neighbors
Adaptive systems
autoencoder
Clustering
Clustering algorithms
Clustering methods
deep clustering
Kernel
Learning
Learning systems
Linearity
Machine learning
Neural networks
parameter-free similarity
Representations
Sparse matrices
structured graph
Subspace methods
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.2978389