Fast Incomplete Multi-View Clustering With View-Independent Anchors

Liu, Suyuan; Liu, Xinwang; Wang, Siwei; Niu, Xiamu; Zhu, En

doi:10.1109/tnnls.2022.3220486

Cited by 15 publications

(7 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this, Liu et al (2022b) and Chen et al (2022b) respectively added rank constraints and orthogonal decomposition terms as constraints to learn the clustering results directly. Li et al (2022), Wang et al (2022) and Liu et al (2022a) applied it to clustering incomplete multi-view datasets. Li et al (2023) considered that noisy features in multi-view data lead to anchor shift during optimization.…”

Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

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: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

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

“…Following this, Wang et al [46] proposed an incomplete large-scale multi-view clustering approach based on the consensus bipartite graph (IMVC-CBG) framework, which integrates anchor selection and anchor graph construction into a unified framework, where all samples share the same anchors and anchor graph to ensure structural consistency across views, enabling fast IMVC tasks. In contrast to view-shared anchors, Liu et al [47] proposed a fast IMVC with view-independent anchors (FIMVC-VIA) method. It learns individual anchors for each view and constructs a unified anchor graph to tackle largescale IMVC tasks.…”

Section: Low-rank Tr Approximationmentioning

confidence: 99%

“…TMBSD [29] t-SVD-based embedding feature × ✓ ✓ × × learning and K-means IMVTSC-MVI [30] t-SVD-based subspace clustering × ✓ ✓ ✓ × TCIMC [27] t-SVD-based subspace clustering × × ✓ × × FIMVC-VIA [47] anchor learning and K-means [46] anchor learning and K-means ✓ × × × × FSR-IMVC [40] TR-based subspace clustering…”

Section: Algorithmsmentioning

confidence: 99%

Feature Space Recovery for Efficient Incomplete Multi-View Clustering

Long,

Zhu,

Comon

et al. 2024

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

T-SVD based incomplete multi-view clustering (IMVC) has received wide attention due to its ability to capture high-order correlations. However, t-SVD suffers from rotation sensitivity, failing to fully explore both inter-and intra-view consistencies. Besides, current methods mainly consider inter-or intra-view correlations, ignoring the low-rank information of sample features within views. To address these weaknesses, we first propose a feature space recovery based IMVC (FSR-IMVC) method, where low-rank feature space recovery and low-rank tensor ring based consistency learning are considered into a unified framework. Furthermore, we extend FSR-IMVC by incorporating anchor learning on the latent feature space, resulting in a scalable FSR-IMVC (sFSR-IMVC) approach that is well-suited to large-scale data. In an iterative way, the learned inter-and intra-view correlations will guide the recovery of missing features, while the explored low-rank information from feature spaces will in turn facilitate consistency exploration, eventually achieving outstanding clustering performance. Experimental results show that FSR-IMVC provides a significant improvement over known state-of-the-art algorithms in terms of ACC, NMI and Purity. Compared with FSR-IMVC, sFSR-IMVC performs slightly worse in clustering accuracy, but offers a notable advantage in computational efficiency, particularly for large-scale datasets. The codes of FSR-IMVC and sFSR-IMVC are publicly available at https://github.com/longzhen520/sFSR-IMVC.

show abstract

“…Traditional MVC models are typically built on the fundamental assumption of complete data and are unable to directly handle missingness in multi-view data (Wen et al 2022). To address these challenges, a series of incomplete multi-view clustering (IMVC) methods have emerged in recent years (Liu et al 2021;Lin et al 2021;Liu et al 2022;He et al 2023;Yang et al 2023). Based on the strategies employed to handle missing data, the existing IMVC methods can be categorized into three types: imputationbased, representation-based, and similarity-based.…”

Section: Introductionmentioning

confidence: 99%

Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering

Liu,

Zhang,

Wen

et al. 2024

AAAI

View full text Add to dashboard Cite

Incomplete multi-view clustering has attracted much attention due to its ability to handle partial multi-view data. Recently, similarity-based methods have been developed to explore the complete relationship among incomplete multi-view data. Although widely applied to partial scenarios, most of the existing approaches are still faced with two limitations. Firstly, fusing similarities constructed individually on each view fails to yield a complete unified similarity. Moreover, incomplete similarity generation may lead to anomalous similarity values with column sum constraints, affecting the final clustering results. To solve the above challenging issues, we propose a Sample-level Cross-view Similarity Learning (SCSL) method for Incomplete Multi-view Clustering. Specifically, we project all samples to the same dimension and simultaneously construct a complete similarity matrix across views based on the inter-view sample relationship and the intra-view sample relationship. In addition, a simultaneously learning consensus representation ensures the validity of the projection, which further enhances the quality of the similarity matrix through the graph Laplacian regularization. Experimental results on six benchmark datasets demonstrate the ability of SCSL in processing incomplete multi-view clustering tasks. Our code is publicly available at https://github.com/Tracesource/SCSL.

show abstract

Fast Incomplete Multi-View Clustering With View-Independent Anchors

Cited by 15 publications

References 56 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

Feature Space Recovery for Efficient Incomplete Multi-View Clustering

Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering

Contact Info

Product

Resources

About