Fast adaptive neighbors clustering via embedded clustering

Liu, Yijun; Cai, Yongda; Yang, Xuemin; Nie, Feiping; Ye, Wujian

doi:10.1016/j.neucom.2020.02.087

Cited by 9 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dhillon (2001) and Nie et al (2017) proposed to apply matrix blocking to co-clustering, thus simplifying the eigenvalue decomposition of the Laplace matrix in Ncut (Shi and Malik 2000) and directly solving the indicator matrix containing the final classification results without post-processing, thus possessing the ability to reduce the complexity of clustering large-scale multi-view data. In recent years, matrix blocking has been widely used in various clustering algorithms due to the need for clustering large-scale multi-view data (Du et al 2023;Yang et al 2022dYang et al , 2023bYuan and Wang 2022;Li and He 2020;Fang et al 2023;Hu et al 2021;Liu et al 2020;Chang et al 2019;Zhang et al 2022a;Nie et al 2019Nie et al , 2021Nie et al , 2017Zhou et al 2022Zhou et al , 2023Zhang and Ma 2022;Lu and Feng 2023;Kang et al 2021;Ren et al 2019). This section first introduces the research background of matrix blocking, and then some excellent algorithms based on matrix blocking to improve computational efficiency are selected to give a brief introduction.…”

Section: Matrix Blocking Based Lsmvcmentioning

confidence: 99%

“…reduce computational complexity, is widely used in many clustering algorithms (Du et al 2023;Yang et al 2022dYang et al , 2023bYuan and Wang 2022;Li and He 2020;Fang et al 2023;Hu et al 2021;Liu et al 2020;Chang et al 2019;Zhang et al 2022a;Nie et al 2017Nie et al , 2019Nie et al , 2021Zhou et al 2022Zhou et al , 2023Zhang and Ma 2022;Lu and Feng 2023;Kang et al 2021;Ren et al 2019). A selection of representative algorithms will be outlined below.…”

Section: Algorithms Based On Matrix Blockingmentioning

confidence: 99%

“…first constructed bipartite graphs for each view based on anchors, and then learns bipartite graphs with k-connected components by directly weighting the bipartite graphs of each view jointly with rank constraints. Unlike constructing similar graphs using anchors directly, Liu et al (2020) constructed similarity graph by selecting anchors from the graph embedding data of the original data, and then optimized the similarity graphs by combining rank constraints. Zhang et al (2022a) proposed to learn the similarity matrix between samples and anchors based on the transformation matrix.…”

Section: Algorithms Based On Matrix Blockingmentioning

confidence: 99%

See 2 more Smart Citations

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

View full text Add to dashboard Cite

The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

show abstract

Section: Matrix Blocking Based Lsmvcmentioning

confidence: 99%

Section: Algorithms Based On Matrix Blockingmentioning

confidence: 99%

Section: Algorithms Based On Matrix Blockingmentioning

confidence: 99%

See 1 more Smart Citation

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

Joint learning of fuzzy embedded clustering and non-negative spectral clustering

Ye,

Wang,

Cai

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

Preprocessed Spectral Clustering with Higher Connectivity for Robustness in Real-World Applications

Sadjadi,

Torra,

Jamshidi

2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

This paper introduces a novel model for spectral clustering to solve the problem of poor connectivity among points within the same cluster as this can negatively impact the performance of spectral clustering. The proposed method leverages both sparsity and connectivity properties within each cluster to find a consensus similarity matrix. More precisely, the proposed approach considers paths of varying lengths in the graph, computing a similarity matrix for each path, and generating a cluster for each path. By combining these clusters using multi-view spectral clustering, the method produces clusters of good quality and robustness when there are outliers and noise. The extracted multiple independent views from different paths in the graph are integrated into a consensus graph. The performance of the proposed method is evaluated on various benchmark datasets and compared to state-of-the-art techniques.

show abstract

Fast adaptive neighbors clustering via embedded clustering

Cited by 9 publications

References 14 publications

The methods for improving large-scale multi-view clustering efficiency: a survey

The methods for improving large-scale multi-view clustering efficiency: a survey

Joint learning of fuzzy embedded clustering and non-negative spectral clustering

Preprocessed Spectral Clustering with Higher Connectivity for Robustness in Real-World Applications

Contact Info

Product

Resources

About