Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering

Yu, Xiao; Liu, Hui; Lin, Yuxiu; Wu, Yan; Zhang, Caiming

doi:10.1016/j.patcog.2022.108772

Cited by 22 publications

(6 citation statements)

References 25 publications

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“…Complexity Analysis. Our method consists of two stages, namely, construction of hypergraphs and optimization by iteratively solving Equation (16). The cost of constructing the initial k-nearest neighbors graph is O 1 (mn 2 d + mn 2 log(n)).…”

Section: Resultsmentioning

confidence: 99%

“…As one of the most extensively researched multi-view clustering techniques, graph-based clustering has exceptionally characterized sample relationships and elucidated intricate data structures. At the heart of graph-based clustering [15,16] lies the construction of high-quality graphs, which has been the focus of numerous studies. Currently, numerous graph-oriented multi-view clustering methods have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…where Q and T (v) are used to indicate Lagrange multiplier while µ > 0 and γ > 0 denote the penalty factor. We use an alternate optimization strategy to solve (16), which involves the following subproblems.…”

Section: Optimizationmentioning

confidence: 99%

“…• Solving S (v) with fixed K (v) and T (v) . According to (16), the solution to this subproblem can be calculated as follows:…”

Section: Optimizationmentioning

confidence: 99%

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Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering

Yang,

Li,

Yun

et al. 2023

Electronics

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Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

“…• Solving S (v) with fixed K (v) and T (v) . According to (16), the solution to this subproblem can be calculated as follows:…”

Section: Optimizationmentioning

confidence: 99%

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Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering

Yang,

Li,

Yun

et al. 2023

Electronics

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show abstract

“…As the next step, we plan to simultaneously consider the time-frequency information, spatial information, and other variables of the time series [45][46][47] , instead of just utilizing the time-frequency information to perform time series spatio-temporal prediction, aiming to comprehensively mining important information from time series data and achieve better prediction results on more data sets.…”

Section: Discussionmentioning

confidence: 99%

Dual‐stage time series analysis on multifeature adaptive frequency domain modeling

Liu

Lin

Liu

et al. 2022

Int J of Intelligent Sys

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Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short-term series forecasting relies more on high-frequency components, while long-term forecasting focuses more on low-frequency data. To better describe the multifrequency mode, a dual-stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end-to-end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state-of-the-art 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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Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering

Cited by 22 publications

References 25 publications

Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering

Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering

Dual‐stage time series analysis on multifeature adaptive frequency domain modeling

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

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