Efficient Clustering Network Based on Matrix Factorization

Jieren Cheng^1,3, Jimei Li^1,3,*, Faqiang Zeng^1,3, Zhicong Tao^1,3, Yue Yang^2,3
1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China
2 School of Cyberspace Security, Hainan University, Haikou, 570228, China
3 Hainan Blockchain Technology Engineering Research Center, Haikou, 570228, China
* Corresponding Author: Jimei Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.051816

Received 15 March 2024; Accepted 21 May 2024; Published online 28 June 2024

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Abstract

Contrastive learning is a significant research direction in the field of deep learning. However, existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods. To address these challenges, we propose the Efficient Clustering Network based on Matrix Factorization (ECN-MF). Specifically, we design a batched low-rank Singular Value Decomposition (SVD) algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data. Additionally, we design a Mutual Information-Enhanced Clustering Module (MI-ECM) to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart. Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

Keywords

Contrastive learning; clustering; matrix factorization

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Efficient Clustering Network Based on Matrix Factorization

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