Cross-view Graph Matching Guided Anchor Alignment for Incomplete Multi-view Clustering

Li, Xingfeng; Sun, Yinghui; Sun, Quansen; Ren, Zhenwen; Sun, Yuan

doi:10.1016/j.inffus.2023.101941

Cited by 31 publications

(2 citation statements)

References 30 publications

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“…Recently, some scholars [28] have also optimized clustering effects from the perspective of the representation learning layer. For instance, Lv et al [29] employed pseudo-label supervised similarity learning to enhance clustering performance, while Li et al [30] optimized clustering effects using a multi-view approach. Unlike their work, which improves performance by refining the downstream clustering task, our paper focuses on enhancing the feature extraction capabilities of autoencoders in the upstream task to subsequently improve the downstream clustering task.…”

Section: Related Work 21 Deep Subspace Clusteringmentioning

confidence: 99%

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

Qian,

Jiang,

Hong

et al. 2024

CMC

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In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data, resulting in more accurate clustering performance. To address the difficulty of hyperparameter selection in deep subspace clustering, this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering, prior knowledge constraints, and model loss weights. Extensive experiments on standard clustering datasets, including ORL, Coil20, and Coil100, validate the effectiveness of the MAS-DSC algorithm. The results show that with its multi-scale network structure and Bayesian hyperparameter optimization, MAS-DSC achieves excellent clustering results on these datasets. Furthermore, tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

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Section: Related Work 21 Deep Subspace Clusteringmentioning

confidence: 99%

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

Qian,

Jiang,

Hong

et al. 2024

CMC

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show abstract

“…With the continuous development of data collection technology, multi-view data have become increasingly ubiquitous for applications in the fields of machine learning and artificial intelligence. [1][2][3][4][5] In general, multi-view data are collected from diverse domains or different feature spaces. For example, a person can be identified by face, fingerprint, and signature.…”

Section: Introductionmentioning

confidence: 99%

Enhanced intra-inter view correlation learning for multi-view subspace clustering

Zhang,

Zhao

et al. 2023

J. Electron. Imag.

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Multi-view subspace clustering has attracted widespread attention for its superior clustering effectiveness. However, most of the current methods do not fully exploit the intra-view (sample-to-sample) and inter-view (view-to-view) information among multi-view data. Focusing on this problem, we propose a method called enhanced intra-inter view correlation learning for multi-view subspace clustering (EIVCL). EIVCL simultaneously considers the intra-view and inter-view correlations and introduces two constraint terms for each aspect to capture the data structure information. Specifically, in the intra-view space, we apply the tensor-singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on the tensor formed by stacking self-representative coefficient matrices for obtaining the high-order correlation information between samples in the specific view. Moreover, we introduce a hypergraphinduced Laplace regularization term to preserve the local geometric structure within views. In the inter-view space, we impose the t-SVD-based TNN on the rotated tensor to obtain the multiple views correlation. Furthermore, we utilize the kernel dependence metric, namely Hilbert-Schmidt independence criterion, to capture the high-order non-linear relationships between views. In addition, all above strategies are integrated into a unified clustering framework, which is solved by our proposed optimization algorithm based on the alternating direction method of multipliers. Extensive experiments on six benchmark datasets demonstrate that EIVCL outperforms several state-of-the-art multi-view algorithms.

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Incomplete multi-view learning: Review, analysis, and prospects

Tang,

Yi,

et al. 2024

Applied Soft Computing

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Cross-view Graph Matching Guided Anchor Alignment for Incomplete Multi-view Clustering

Cited by 31 publications

References 30 publications

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

Enhanced intra-inter view correlation learning for multi-view subspace clustering

Incomplete multi-view learning: Review, analysis, and prospects

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