• 人工智能 •

### 结合降噪和自注意力的深度聚类算法

1. 1. 河北大学 数学与信息科学学院 河北省机器学习与计算智能重点实验室，河北 保定 071002
2. 北京师范大学珠海分校 应用数学学院，广东 珠海 519087
• 出版日期:2021-09-01 发布日期:2021-09-06

### Deep Clustering Algorithm Based on Denoising and Self-Attention

CHEN Junfen, ZHANG Ming, ZHAO Jiacheng, XIE Bojun, LI Yan

1. 1. Hebei Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding, Hebei 071002, China
2. School of Applied Mathematics, Beijing Normal University Zhuhai, Zhuhai, Guangdong 519087, China
• Online:2021-09-01 Published:2021-09-06

Abstract:

Recently, deep clustering methods have achieved perfect clustering performances, which simultaneously perform clusters assignment and features representation learning. However, the performances greatly degenerate with the decreasing of images quality such as noisy image. To this end, a novel deep clustering method DDC (deep denoising clustering) is proposed. A deep convolutional denoising auto-encoder is employed to learn the robust features representation from noisy image, and self-attention mechanism improves the ability of capturing local features. End-to-end jointly training obtains features more suitable to clustering tasks and then completes clustering assignment. The similarities between feature embeddings and cluster-centers are weighted by different coefficients to enlarge the differences between intra-clusters and inter-clusters. The experimental results on the public datasets demonstrate that the proposed DDC can provide better clustering performances. And compared with other deep clustering algorithms, for example, the clustering accuracy of DDC is 0.803 while DEC (deep embedding clustering) is 0.597 on the COIL-20 dataset. Overall, DDC algorithm with the help of deep convolutional denoising auto-encoder and self-attention can efficiently group noisy images, and further enlarges the application range of deep clustering analysis.