Robust landmark graph-based clustering for high-dimensional data

Yang, Ben; Wu, Jinghan; Sun, Aoran; Gao, Naying; Zhang, Xuetao

doi:10.1016/j.neucom.2022.05.011

Cited by 4 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for improving the robustness of real-world data clustering tasks, it is currently widely adopted to use the robustness norm to measure the error between the original data and the reconstructed representation. For example, the L 1 -norm-based methods [27,28] and the method based on the L 21 -norm-based methods [29][30][31]. LSSC [27] uses the L 1 -norm to define a sparse coding problem to improve the robustness of the representation, RDCF [28] uses the L 1 -norm to minimize the error before and after the conceptual decomposition, and the L 21norm is used to select features to constrain row sparsity by enhancing the matrix and constrain the errors of the subspace representation and the original data in LSS [29] and LRR [30], respectively.…”

Section: Introductionmentioning

confidence: 99%

Robust large-scale clustering based on correntropy

2022

View full text Add to dashboard Cite

With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research on robust large-scale real-world data clustering algorithms has become one of the hottest topics. In response to this issue, a robust large-scale clustering algorithm based on correntropy (RLSCC) is proposed in this paper, specifically, k-means is firstly applied to generated pseudo-labels which reduce input data scale of subsequent spectral clustering, then anchor graphs instead of full sample graphs are introduced into spectral clustering to obtain final clustering results based on pseudo-labels which further improve the efficiency. Therefore, RLSCC inherits the advantages of the effectiveness of k-means and spectral clustering while greatly reducing the computational complexity. Furthermore, correntropy is developed to suppress the influence of noises and outlier the real-world data on the robustness of clustering. Finally, extensive experiments were carried out on real-world datasets and noise datasets and the results show that compared with other state-of-the-art algorithms, RLSCC can improve efficiency and robustness greatly while maintaining comparable or even higher clustering effectiveness.

show abstract