Large-Scale Subspace Clustering Based on Purity Kernel Tensor Learning

In conventional subspace clustering methods, affinity matrix learning and spectral clustering algorithms are widely used for clustering tasks. However, these steps face issues, including high time consumption and spatial complexity, making large-scale subspace clustering (LS2C) tasks challenging to...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 13; no. 1; p. 83
Main Authors: Zheng, Yilu, Zhao, Shuai, Zhang, Xiaoqian, Xu, Yinlong, Peng, Lifan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2024
Subjects:
Accuracy
Affinity
Algorithms
Clustering
Clustering (Computers)
Coders
Datasets
Eigenvectors
Electronic data processing
Machine learning
Mathematical analysis
Methods
Subspace methods
Subspaces
Task complexity
Tensors
China
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:In conventional subspace clustering methods, affinity matrix learning and spectral clustering algorithms are widely used for clustering tasks. However, these steps face issues, including high time consumption and spatial complexity, making large-scale subspace clustering (LS2C) tasks challenging to execute effectively. To address these issues, we propose a large-scale subspace clustering method based on pure kernel tensor learning (PKTLS2C). Specifically, we design a pure kernel tensor learning (PKT) method to acquire as much data feature information as possible while ensuring model robustness. Next, we extract a small sample dataset from the original data and use PKT to learn its affinity matrix while simultaneously training a deep encoder. Finally, we apply the trained deep encoder to the original large-scale dataset to quickly obtain its projection sparse coding representation and perform clustering. Through extensive experiments on large-scale real datasets, we demonstrate that the PKTLS2C method outperforms existing LS2C methods in clustering performance.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13010083