Self-paced Learning for K-means Clustering Algorithm

• Published in: Pattern recognition letters Vol. 132; pp. 69 - 75
• Main Authors: Yu, Hao, Wen, Guoqiu, Gan, Jiangzhang, Zheng, Wei, Lei, Cong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2020; Elsevier Science Ltd
• Subjects: Algorithms; Cluster analysis; Clustering; Learning; Learning theory; Machine learning; Outliers (statistics); Training; Vector quantization; 65D05; 65D17; 41A05; 41A10
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.08.028

•Propose a novel clustering algorithm by adding the self-paced regularization factor.•Improve the problem of non-convex optimization is easy to fall into local optimal solution.•A linear self-paced regularization factor is used to distinguish between noise and normal samples. The traditional K-means clustering algorithm is easily affected by the noise, outliers and falling into local optimal solution. This paper proposes a K-means clustering algorithm based on self-paced learning. Firstly, a best training subset is selected to construct the initial cluster model base on self-paced learning theory, and then enhances the generalization ability of the initial clustering model by adding sub-good subsets of samples one by one until the model performance is optimal or all training samples are used up. By analyzing the experimental results, the clustering algorithm proposed in this paper achieves better performance than the compare algorithms on the five real data sets.