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Cui J, He Z, Huang Q, Fu Y, Li Y, Wen J. Structure-aware contrastive hashing for unsupervised cross-modal retrieval. Neural Netw 2024; 174:106211. [PMID: 38447425 DOI: 10.1016/j.neunet.2024.106211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/25/2023] [Accepted: 02/23/2024] [Indexed: 03/08/2024]
Abstract
Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational efficiency and low storage overhead. However, constructing similarity relationship among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix across modalities. Moreover, we design a multimodal structure-aware alignment network to minimize heterogeneous gap in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.
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Affiliation(s)
- Jinrong Cui
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Zhipeng He
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Qiong Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China; Guangzhou Key Laboratory of Intelligent Agricuture, Guangzhou, China
| | - Yulu Fu
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Yuting Li
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Jie Wen
- Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China.
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Dornaika F, El Hajjar S. Towards a unified framework for graph-based multi-view clustering. Neural Netw 2024; 173:106197. [PMID: 38422834 DOI: 10.1016/j.neunet.2024.106197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 11/12/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024]
Abstract
Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering can be divided into several categories: Spectral clustering algorithms, subspace multi-view clustering algorithms, matrix factorization approaches, and kernel methods. Despite the high performance of these methods, they directly fuse all similarity matrices of all views and separate the affinity learning process from the multiview clustering process. The performance of these algorithms can be affected by noisy affinity matrices. To overcome this drawback, this paper presents a novel method called One Step Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (OSMGNE). Instead of directly merging the similarity matrices of different views, which may contain noise, a step of learning a consensus similarity matrix is performed. This step forces the similarity matrices of different views to be too similar, which eliminates the problem of noisy data. Moreover, the use of the nonnegative embedding matrix (soft cluster assignment matrix makes it possible to directly obtain the final clustering result without any extra step. The proposed method can solve five subtasks simultaneously. It jointly estimates the similarity matrix of all views, the similarity matrix of each view, the corresponding spectral projection matrix, the unified clustering indicator matrix, and automatically gives the weight of each view without the use of hyper-parameters. In addition, another version of our method is also studied in this paper. This method differs from the first one by using a consensus spectral projection matrix and a consensus Laplacian matrix over all views. An iterative algorithm is proposed to solve the optimization problem of these two methods. The two proposed methods are tested on several real datasets, which prove their superiority.
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Affiliation(s)
- F Dornaika
- University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain; Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam.
| | - S El Hajjar
- University of the Basque Country UPV/EHU, San Sebastian, Spain
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Chen R, Tang Y, Zhang W, Feng W. Adaptive-weighted deep multi-view clustering with uniform scale representation. Neural Netw 2024; 171:114-126. [PMID: 38091755 DOI: 10.1016/j.neunet.2023.11.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 10/07/2023] [Accepted: 11/29/2023] [Indexed: 01/29/2024]
Abstract
Multi-view clustering has attracted growing attention owing to its powerful capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally fail to distinguish the unequal importance of multiple views to the clustering task and overlook the scale uniformity of learned latent representation among different views, resulting in blurry physical meaning and suboptimal model performance. To address these issues, in this paper, we propose a joint learning framework, termed Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU). Specifically, to achieve more reasonable multi-view fusion, we introduce an adaptive weighting strategy, which imposes simplex constraints on heterogeneous views for measuring their varying degrees of contribution to consensus prediction. Such a simple yet effective strategy shows its clear physical meaning for the multi-view clustering task. Furthermore, a novel regularizer is incorporated to learn multiple latent representations sharing approximately the same scale, so that the objective for calculating clustering loss cannot be sensitive to the views and thus the entire model training process can be guaranteed to be more stable as well. Through comprehensive experiments on eight popular real-world datasets, we demonstrate that our proposal performs better than several state-of-the-art single-view and multi-view competitors.
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Affiliation(s)
- Rui Chen
- College of Information Science and Technology, Hainan University, Haikou, 570208, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Yongqiang Tang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Wensheng Zhang
- College of Information Science and Technology, Hainan University, Haikou, 570208, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Wenlong Feng
- College of Information Science and Technology, Hainan University, Haikou, 570208, China; State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570208, China.
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Sun L, Wen J, Liu C, Fei L, Li L. Balance guided incomplete multi-view spectral clustering. Neural Netw 2023; 166:260-272. [PMID: 37531726 DOI: 10.1016/j.neunet.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/04/2023]
Abstract
There is a large volume of incomplete multi-view data in the real-world. How to partition these incomplete multi-view data is an urgent realistic problem since almost all of the conventional multi-view clustering methods are inapplicable to cases with missing views. In this paper, a novel graph learning-based incomplete multi-view clustering (IMVC) method is proposed to address this issue. Different from existing works, our method aims at learning a common consensus graph from all incomplete views and obtaining a clustering indicator matrix in a unified framework. To achieve a stable clustering result, a relaxed spectral clustering model is introduced to obtain a probability consensus representation with all positive elements that reflect the data clustering result. Considering the different contributions of views to the clustering task, a weighted multi-view learning mechanism is introduced to automatically balance the effects of different views in model optimization. In this way, the intrinsic information of the incomplete multi-view data can be fully exploited. The experiments on several incomplete multi-view datasets show that our method outperforms the compared state-of-the-art clustering methods, which demonstrates the effectiveness of our method for IMVC.
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Affiliation(s)
- Lilei Sun
- School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, 518000, China
| | - Jie Wen
- Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, 518000, China.
| | - Chengliang Liu
- Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, 518000, China
| | - Lunke Fei
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510000, China
| | - Lusi Li
- Department of Computer Science, Old Dominion University, USA
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Yin J, Jiang J. Incomplete Multi-view Clustering Based on Self-representation. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11172-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2895338. [PMID: 36507228 PMCID: PMC9729040 DOI: 10.1155/2022/2895338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/23/2022] [Accepted: 11/04/2022] [Indexed: 12/02/2022]
Abstract
Incomplete pattern clustering is a challenging task because the unknown attributes of the missing data introduce uncertain information that affects the accuracy of the results. In addition, the clustering method based on the single view ignores the complementary information from multiple views. Therefore, a new belief two-level weighted clustering method based on multiview fusion (BTC-MV) is proposed to deal with incomplete patterns. Initially, the BTC-MV method estimates the missing data by an attribute-level weighted imputation method with k-nearest neighbor (KNN) strategy based on multiple views. The unknown attributes are replaced by the average of the KNN. Then, the clustering method based on multiple views is proposed for a complete data set with estimations; the view weights represent the reliability of the evidence from different source spaces. The membership values from multiple views, which indicate the probability of the pattern belonging to different categories, reduce the risk of misclustering. Finally, a view-level weighted fusion strategy based on the belief function theory is proposed to integrate the membership values from different source spaces, which improves the accuracy of the clustering task. To validate the performance of the BTC-MV method, extensive experiments are conducted to compare with classical methods, such as MI-KM, MI-KMVC, KNNI-FCM, and KNNI-MFCM. Results on six UCI data sets show that the error rate of the BTC-MV method is lower than that of the other methods. Therefore, it can be concluded that the BTC-MV method has superior performance in dealing with incomplete patterns.
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One-step incomplete multiview clustering with low-rank tensor graph learning. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.103054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Zhang H, Chen X, Zhang E, Wang L. Incomplete Multi-view Learning via Consensus Graph Completion. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10973-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhao X, Gu B, Li Q, Li J, Zeng W, Li Y, Guan Y, Huang M, Lei L, Zhong G. Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery. Front Cardiovasc Med 2022; 9:962992. [PMID: 36061544 PMCID: PMC9434347 DOI: 10.3389/fcvm.2022.962992] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022] Open
Abstract
Background Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS. Methods The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters. Results A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97–4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9–8.5)]. Conclusion Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.
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Affiliation(s)
- Xu Zhao
- Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Bowen Gu
- Laboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Qiuying Li
- Laboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Jiaxin Li
- Laboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Weiwei Zeng
- Department of Pharmacy, The Second People's Hospital of Longgang District, Shenzhen, China
| | - Yagang Li
- Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Yanping Guan
- Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Min Huang
- Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Liming Lei
- Laboratory of South China Structural Heart Disease, Department of Intensive Care Unit of Cardiovascular Suregery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou, China
- *Correspondence: Liming Lei
| | - Guoping Zhong
- Department of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
- Guoping Zhong
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Clustering via multiple kernel k-means coupled graph and enhanced tensor learning. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03679-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments. ELECTRONICS 2022. [DOI: 10.3390/electronics11050712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.
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