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Applied Sciences
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Algorithms and Applications of Multi-View Information Clustering
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Algorithms and Applications of Multi-View Information Clustering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 8579

Special Issue Editor

Dr. Huibing Wang

E-Mail Website
Guest Editor
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: machine learning; computer vision

Special Issue Information

Dear Colleagues,

Along with the development of the multimedia era, a large amount of multi-view data needs to be processed in various applications and research, such as computer vision, machine learning, data mining, and other fields. A typical application is multi-view clustering, which aims to effectively exploit consistency and complementary information from different views and partition multi-view data into different groups in an unsupervised manner. Due to the diversity of multi-view data, it is crucial to develop a new algorithm to comprehensively mine information from different views and obtain a better clustering performance. The effort devoted to multi-view clustering is supposed to answer the question of how to effectively capture discriminative information in different views for clustering. Unfortunately, however, while some of the aforementioned approaches have achieved great performance, we need faster and more robust algorithms.

This Special Issue of Applied Sciences, entitled “Algorithms and Applications of Multi-view Information Clustering”, will be mainly devoted to (but not limited to) the problems of clustering on multi-view data. We invite you to submit your latest research on both academia and industry.

Dr. Huibing Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • data analysis
  • multi-view clustering
  • theory of computation

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Published Papers (4 papers)

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Research

19 pages, 5716 KiB  
Article
Efficient Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies
by Peng Chen and Huibing Wang
Appl. Sci. 2023, 13(15), 8993; https://doi.org/10.3390/app13158993 - 5 Aug 2023
Viewed by 926
Abstract
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most existing semi-supervised metric [...] Read more.
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most existing semi-supervised metric learning methods rely on the manifold assumptions to mine the rich discriminant information of the unlabeled data, which breaks the intrinsic connection between the manifold regularizer-building process and the subsequent metric learning. Moreover, these methods usually encounter high computational or memory overhead. To solve these issues, we develop a novel method entitled Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies (ISMLP). ISMLP aims to simultaneously learn multiple proxy vectors as well as a Mahalanobis matrix and forms the semi-supervised metric learning as the probability distribution optimization parameterized by the Mahalanobis distance between the instance and each proxy vector. ISMLP maximizes the entropy of the labeled data and minimizes that of the unlabeled data to follow the entropy regularization, in this way, the labeled part and unlabeled part can be integrated in a meaningful way. Furthermore, the time complexity of the proposed method has a linear dependency concerning the number of instances, thereby, can be extended to the large-scale dataset without incurring too much time. Experiments on multiple datasets demonstrate the superiority of the proposed method over the compared methods used in the experiments. Full article
(This article belongs to the Special Issue Algorithms and Applications of Multi-View Information Clustering)
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16 pages, 2387 KiB  
Article
Multi-Attention-Guided Cascading Network for End-to-End Person Search
by Jianxi Yang and Xiaoyong Wang
Appl. Sci. 2023, 13(9), 5576; https://doi.org/10.3390/app13095576 - 30 Apr 2023
Viewed by 1188
Abstract
The key procedure is to accurately identify pedestrians in complex scenes and effectively embed features from multiple vision cues. However, it is still a limitation to coordinate two tasks in the unified framework, thus leading to high computational overhead and unsatisfactory search performance. [...] Read more.
The key procedure is to accurately identify pedestrians in complex scenes and effectively embed features from multiple vision cues. However, it is still a limitation to coordinate two tasks in the unified framework, thus leading to high computational overhead and unsatisfactory search performance. Furthermore, most methods do not take significant clues and key features of pedestrians into consideration. To remedy these issues, we introduce a novel method named Multi-Attention-Guided Cascading Network (MGCN) in this paper. Specifically, we obtain the trusted bounding box through the detection header as the label information for post-process. Based on the end-to-end network, we demonstrate the advantages of jointly learning to construct the bounding box and attention module by maximizing the complementary information from different attention modules, which can achieve optimized person search performance. Meanwhile, by imposing an aligning module on re-id feature extracted network to locate visual clues with semantic information, which can restrain redundant background information. Extensive experimental results for the two benchmark person search datasets are provided to demonstrate that the proposed MGCN markedly outperforms the state-of-the-art baselines. Full article
(This article belongs to the Special Issue Algorithms and Applications of Multi-View Information Clustering)
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13 pages, 6952 KiB  
Article
Three-Dimensional Point Cloud Data Pre-Processing for the Multi-Source Information Fusion in Aircraft Assembly
by Rupeng Li, Weiping He and Siren Liu
Appl. Sci. 2023, 13(8), 4719; https://doi.org/10.3390/app13084719 - 9 Apr 2023
Cited by 1 | Viewed by 1611
Abstract
Wing-body assembly is a key part of aircraft manufacturing, and during the process of wing assembly, the 3D point cloud data of the components are an important basis for attitude adjustment. The large amount of measured point cloud data and the obvious noise [...] Read more.
Wing-body assembly is a key part of aircraft manufacturing, and during the process of wing assembly, the 3D point cloud data of the components are an important basis for attitude adjustment. The large amount of measured point cloud data and the obvious noise affect the quality and efficiency of the final assembly. To address this problem, research on the pre-processing method of the component point cloud data is carried out. Firstly, a feature-enhanced point cloud resampling method is proposed to preserve key features such as part contours in the resampling process. Then, a multi-scale point cloud data noise filtering method is proposed, which can effectively filter out the outliers. The experimental results show that the proposed method improves the speed and accuracy of the subsequent point cloud analysis effectively and is successfully applied to the assembly process of a large passenger aircraft, laying the foundation for high-quality assembly. Full article
(This article belongs to the Special Issue Algorithms and Applications of Multi-View Information Clustering)
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13 pages, 5140 KiB  
Article
Deep Image Clustering Based on Label Similarity and Maximizing Mutual Information across Views
by Feng Peng and Kai Li
Appl. Sci. 2023, 13(1), 674; https://doi.org/10.3390/app13010674 - 3 Jan 2023
Cited by 1 | Viewed by 3945
Abstract
Most existing deep image clustering methods use only class-level representations for clustering. However, the class-level representation alone is not sufficient to describe the differences between images belonging to the same cluster. This may lead to high intra-class representation differences, which will harm the [...] Read more.
Most existing deep image clustering methods use only class-level representations for clustering. However, the class-level representation alone is not sufficient to describe the differences between images belonging to the same cluster. This may lead to high intra-class representation differences, which will harm the clustering performance. To address this problem, this paper proposes a clustering model named Deep Image Clustering based on Label Similarity and Maximizing Mutual Information Across Views (DCSM). DCSM consists of a backbone network, class-level and instance-level mapping block. The class-level mapping block learns discriminative class-level features by selecting similar (dissimilar) pairs of samples. The proposed extended mutual information is to maximize the mutual information between features extracted from views that were obtained by using data augmentation on the same image and as a constraint on the instance-level mapping block. This forces the instance-level mapping block to capture high-level features that affect multiple views of the same image, thus reducing intra-class differences. Four representative datasets are selected for our experiments, and the results show that the proposed model is superior to the current advanced image clustering models. Full article
(This article belongs to the Special Issue Algorithms and Applications of Multi-View Information Clustering)
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