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Article
Computer Science, Artificial Intelligence
Guo Zhong et al.
Summary: This article proposes a novel matrix factorization framework, called Joint Hypergraph Embedding and Sparse Coding (JHESC), which captures high-order semantic information in data. Experimental results demonstrate that the proposed method consistently outperforms other state-of-the-art matrix factorization methods in data clustering.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Lele Fu et al.
Summary: In this study, we propose a unified model for multi-view intrinsic subspace clustering. The model addresses the limitations of existing methods by learning multiple intrinsic subspace representations and employing low-rank tensor approximation. The clustering results are used to guide the optimization process. Experimental results demonstrate the superiority of the proposed method over other approaches.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xuanlong Ma et al.
Summary: This paper proposes a unified learning framework that combines multi-graph construction, consensus graph construction, and clustering to directly provide clustering results for multiview data. Each view is treated differently through automatic weight learning.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Dong Wei et al.
Summary: An unsupervised robust dimensionality reduction algorithm for Grassmann manifold is proposed in this paper, leveraging strategies to improve accuracy and efficiency. By utilizing a coefficients-based similarity graph and a unified learning framework, accurate structures and discriminative projections can be obtained simultaneously.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Zexi Chen et al.
Summary: Multi-view clustering has gained attention for its ability to leverage the complementarity of multi-view data. However, existing methods suffer from issues such as insufficient discriminative power, feature redundancy, and degraded clustering performance. This paper proposes a multi-view clustering framework with a diversity loss for deep matrix factorization to address these issues effectively. Experimental results demonstrate the superiority of the proposed method over existing methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xiaoshuang Sang et al.
Summary: A novel consensus graph-based auto-weighted multi-view projection clustering approach is proposed in this paper, which addresses the noise and redundancy issues in multi-view data clustering through consensus structured graph learning, automatic weighting, and manifold structure preservation strategies, showing promising results.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zhongyan Gui et al.
Summary: The multi-view clustering method LRCP learns an enhanced consensus representation by preserving the correlation of latent representations to improve clustering performance. The method uses an adaptive neighbors graph to construct initial affinity matrices and integrates the consensus representation in the original space and the shared latent representation in the latent embedding space into a unified optimization framework to learn an enhanced consensus representation with enriched information and improved representation capability.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohuan Lu et al.
Summary: In this paper, a novel method called LPP_SGE is proposed for unsupervised dimensionality reduction. LPP_SGE introduces a novel adaptive graph learning model and obtains the intrinsic graph and projection in a unified framework by fully exploring the representation information and distance information of the original data. It simultaneously captures the representation information and distance information in one term. Moreover, LPP_SGE enhances the robustness by introducing an 'l2,1' norm based projection constraint to select the most discriminative features from the complex data.
PATTERN RECOGNITION
(2022)
Article
Automation & Control Systems
Xiaozhao Fang et al.
Summary: Cross-modal retrieval has attracted attention in large-scale multimedia databases for its efficiency and effectiveness. This article proposes a novel method called average approximate hashing (AAH) which integrates locality preservation, residual preservation, and projection into different semantic spaces to address the shortcomings of traditional methods. Results show that AAH outperforms state-of-the-art cross-modal hashing methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Zhenglai Li et al.
Summary: This novel multi-view clustering method constructs an essential similarity graph in a spectral embedding space, addressing the issue of noise and redundancy from learning similarity graph directly from original features. By imposing a weighted tensor nuclear norm constraint, it captures high-order consistent information and effectively handles clustering of multi-view datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Information Systems
Peiguang Jing et al.
Summary: This paper proposes a robust multi-view affinity graph learning framework that can effectively handle data from multiple sources through improved feature selection algorithm and Consistent Affinity Graph Learning algorithm. Experimental results demonstrate that the proposed method achieves promising results on publicly available datasets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Xiuyi Jia et al.
Summary: Label distribution learning (LDL) is a new paradigm in machine learning that addresses label ambiguity by emphasizing the relevance of each label to a particular instance. We propose a projection graph embedding algorithm for semi-supervised label distribution learning (PGE-SLDL), which aims to select valuable features, construct an accurate graph, and recover unknown label distributions. The self-updating projection graph is more effective in learning label distribution compared to traditional fixed graphs in semi-supervised learning.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zhao Kang et al.
Summary: Graph-based clustering and semi-supervised classification techniques have shown impressive performance in modeling structures and interactions. This paper proposes a graph learning framework that preserves both local and global structure of data, outperforming other state-of-the-art methods in extensive experiments. Theoretical analysis reveals the equivalence of the proposed model to a combination of kernel k-means and k-means methods under certain conditions.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Zhenwen Ren et al.
Summary: The proposed multi-view latent energy-preserving embedding (MLEE) method aims to integrate multi-view data and alleviate the issues caused by high-dimensionality, achieving state-of-the-art clustering performance by learning low-dimensional clean data and consensus affinity graph learning.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zhonghua Liu et al.
Summary: Many methods based on matrix factorization have been proposed recently, with Latent Low-rank Representation (LatLRR) being a powerful feature extraction method. However, LatLRR and its variants have shortcomings, leading to the proposal of Robust Sparse Low-rank Embedding (RSLRE) as a solution. With the introduction of L-2, L-1 norm constraints, RSLRE algorithm can adaptively select discriminative and robust data features.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Xiao Yu et al.
Summary: Recent advancements in multi-view clustering have attracted significant attention. Many existing methods suffer from high time complexity or difficulty in tuning their parameters. Our proposed method, FITS-MSC, introduces a fine-grained similarity fusion strategy to address the issues that may arise from assigning the same weight to instances in partial views.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Zhan Wang et al.
Summary: The paper proposes an unsupervised subspace learning method for multiple feature fusion, which uncovers common structure information of multiple features, handles redundant features and demonstrates robustness and superior performance.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
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Zhao Kang et al.
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Guang-Yu Zhang et al.
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2020)
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Zhanxuan Hu et al.
INFORMATION FUSION
(2020)
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Shudong Huang et al.
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(2020)
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Hao Wang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
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Tao Zhou et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
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Lele Fu et al.
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Shi-Xun Lin et al.
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Haoran Li et al.
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IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
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IEEE TRANSACTIONS ON CYBERNETICS
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(2018)
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
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(2017)
Proceedings Paper
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30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
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IEEE TRANSACTIONS ON IMAGE PROCESSING
(2016)
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JB Shi et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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