Incomplete Multi-view Clustering

Gao, Hang; Peng, Yuxing; Jian, Songlei

doi:10.1007/978-3-319-48390-0_25

Cited by 26 publications

(11 citation statements)

References 11 publications

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“…(Qian et al 2016) developed a double constrained framework called DCNMF by incorporating the cluster similarity and manifold preserving constraints. (Gao, Peng, and Jian 2016) gave an IVC algorithm for clustering with more than two incomplete views, which was based on spectral graph theory and kernel alignment principle. Recently, (Xu et al 2018) sought a latent space and then performed data reconstruction for partial multiview subspace representation.…”

Section: Related Workmentioning

confidence: 99%

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Guo

2019

AAAI

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Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Guo

2019

AAAI

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“…However, it requires at least one complete view as reference for view completion. Gao et al proposed to learn the consensus representation based on the kernel alignment (Gao, Peng, and Jian 2016). However, this method can not handle the case with large incomplete rate of views.…”

Section: Introductionmentioning

confidence: 99%

Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

Wen

Zhang

et al. 2019

AAAI

175

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Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored;3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

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“…However, it is impossible to construct a complete graph connecting all samples due to the lack of partial samples in incomplete multi-view clustering. To cover this problem, Gao et al [ 39 ] first fill the missing parts and then learn graphs and representations. Zhao et al [ 36 ] utilize NMF to obtain consistent representations to guide the generation of graphs with local structures.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

Zhang

Wang

et al. 2020

Sensors

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With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.

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Incomplete Multi-view Clustering

Cited by 26 publications

References 11 publications

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

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