Article
Automation & Control Systems
Xiangli Li, Xiyan Lu, Xuezhen Fan
Summary: Nonnegative matrix factorization (NMF) is an effective method for high dimensional data analysis, but it cannot utilize label information. To address this, a semi-supervised sparse neighbor constrained co-clustering model (SSCCDS) is proposed. By introducing co-clustering and regularization constraints, SSCCDS overcomes the limitations of traditional NMF and achieves good clustering performance, as demonstrated by experiments on different datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Zhang DongPing, Luo YiHao, Yu YuYuan, Zhao QiBin, Zhou GuoXu
Summary: This study proposes a new semi-supervised multi-view clustering method and demonstrates its effectiveness through experiments. The method learns the complex manifold structure of multi-view data by constructing hypergraphs and effectively utilizes label information to improve clustering performance.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Changpeng Wang, Jiangshe Zhang, Tianjun Wu, Meng Zhang, Guang Shi
Summary: In this paper, a novel SNMF method called Positive and Negative Label Propagations based SNMF (PNLP-SNMF) is proposed to improve clustering performance. The method leverages both positive and negative label information and achieves nonnegative matrix factorization and label constraint propagation in a unified optimization model. Experimental results demonstrate the effectiveness of PNLP-SNMF in image clustering tasks.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wenhui Wu, Junhui Hou, Shiqi Wang, Sam Kwong, Yu Zhou
Summary: In this paper, we propose a semi-supervised adaptive kernel concept factorization (SAKCF) method that integrates data representation and kernel learning and solves the problem using an alternating iterative algorithm. Experimental results demonstrate the effectiveness and advantages of SAKCF over other methods in clustering tasks.
PATTERN RECOGNITION
(2023)
Article
Engineering, Electrical & Electronic
Siyuan Peng, Jingxing Yin, Zhijing Yang, Badong Chen, Zhiping Lin
Summary: This paper proposes a framework named MVCHSS for multiview data clustering, which improves clustering performance by constructing a set of informative similarity matrices and incorporating graph regularization without the need for additional post-processing.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Ying Zhang, Xiangli Li, Mengxue Jia
Summary: Traditional clustering is an unsupervised learning method, but prior information in actual data can be used for semi-supervised clustering. Pairwise constraints are commonly used prior information that can improve clustering performance. This paper proposes a semi-supervised clustering method that combines pairwise constraints with nonnegative matrix factorization and verifies its effectiveness through experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Siyuan Peng, Wee Ser, Badong Chen, Zhiping Lin
Summary: CSNMF is a novel robust semi-supervised NMF method proposed to address the sensitivity of traditional NMF algorithms to noisy data or underutilization of supervised information. It adopts a correntropy based loss function and uses pointwise and pairwise constraints to obtain discriminative data representation effectively.
PATTERN RECOGNITION
(2021)
Article
Mathematical & Computational Biology
Qing Yang, Jun Chen, Najla Al-Nabhan
Summary: This study proposes a robust constrained nonnegative matrix factorization (RCNMF) approach that learns discriminative representations by integrating global and local structures of data and addresses the issues of noise and outliers. Experimental results demonstrate that RCNMF outperforms other state-of-the-art algorithms.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jovan Chavoshinejad, Seyed Amjad Seyedi, Fardin Akhlaghian Tab, Navid Salahian
Summary: Semi-supervised nonnegative matrix factorization combines the strengths of matrix factorization in learning part-based representation and can achieve high learning performance with limited labeled data and a large amount of unlabeled data. Recent research focuses on utilizing self-supervised learning to enhance semi-supervised learning. This paper proposes an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) model that directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. Experimental results on standard benchmark datasets demonstrate the effectiveness of the proposed model in semi-supervised clustering.
PATTERN RECOGNITION
(2023)
Article
Engineering, Chemical
Kexin Zhang, Lingling Li, Jinhong Di, Yi Wang, Xuezhuan Zhao, Ji Zhang
Summary: This paper proposes a novel NMF-based data representation method, which uses multiple graph adaptive regularization, limited supervised information, and sparse constraint to learn more discriminative data representation and improve the quality of NMF decomposition.
Article
Computer Science, Information Systems
Siyuan Peng, Zhijing Yang, Bingo Wing-Kuen Ling, Badong Chen, Zhiping Lin
Summary: A new semi-supervised NMF method called dual semi-supervised convex nonnegative matrix factorization (DCNMF) is proposed in this paper. DCNMF incorporates the pointwise and pairwise constraints of labeled samples into convex NMF, resulting in a better low-dimensional data representation. It can process mixed-sign data due to the nonnegative constraint only on the coefficient matrix.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Sheng Bi, Xiangli Li
Summary: This paper presents a semi-supervised clustering ensemble based on the genetic algorithm model, which enhances the algorithm's performance by utilizing pairwise constraint information.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jingxing Yin, Siyuan Peng, Zhijing Yang, Badong Chen, Zhiping Lin
Summary: A new semi-supervised symmetric nonnegative matrix factorization (SNMF) method, called hypergraph based semi-supervised SNMF (HSSNMF), is proposed for image clustering. HSSNMF constructs a similarity matrix using a predefined hypergraph and propagates pairwise constraints using a hypergraph-based algorithm. The discriminative assignment matrix is obtained through optimization. Experimental results demonstrate the superiority of HSSNMF compared to other state-of-the-art methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Haonan Huang, Guoxu Zhou, Qibin Zhao, Lifang He, Shengli Xie
Summary: In this article, a novel model called deep autoencoder-like NMF for MRL (DANMF-MRL) is proposed, which considers both multiview consistency and complementarity for a more comprehensive representation. A one-step DANMF-MRL is further proposed, which learns the latent representation and final clustering labels matrix in a unified framework, achieving optimal clustering performance without tedious clustering steps. Two efficient iterative optimization algorithms are developed with theoretical convergence analysis. Extensive experiments on five benchmark datasets demonstrate the superiority of the proposed approaches over other state-of-the-art MRL methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Kexin Zhang, Xuezhuan Zhao, Siyuan Peng
Summary: A novel multiple graph regularized semi-supervised NMF method, MSNMF, is proposed in this paper, which combines limited supervised information in the form of pairwise constraints with multiple graph regularization to capture discriminative data representation. Experimental results on eight practical image datasets demonstrate that MSNMF can achieve better clustering results than several related NMF methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Automation & Control Systems
Xiumei Wang, Tianzhen Zhang, Xinbo Gao
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Computer Science, Artificial Intelligence
Di Wang, Xinbo Gao, Xiumei Wang, Lihuo He
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2019)
Article
Computer Science, Artificial Intelligence
Zheng Hui, Xinbo Gao, Xiumei Wang
Article
Computer Science, Artificial Intelligence
Xiumei Wang, Dingning Guo, Peitao Cheng
Summary: Sequential data clustering is a challenging task in data mining, and subspace clustering is a representative tool for dealing with complex local correlation and high-dimensional structure. It is important to learn a more specific structure representation of a sequence to preserve both sequential information and efficient connections.
PATTERN RECOGNITION
(2022)
Article
Engineering, Electrical & Electronic
Xi Zhang, Xiumei Wang, Peitao Cheng
Summary: This article proposes a new strategy based on contrastive learning to improve the performance of unsupervised image retrieval. By fully utilizing the structural information in semantic similarity and employing a novel framework to handle hash codes with different lengths simultaneously, better image retrieval results are achieved.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Computer Science, Information Systems
Di Wang, Caiping Zhang, Quan Wang, Yumin Tian, Lihuo He, Lin Zhao
Summary: In recent years, cross-modal hashing has become a vital technique in cross-modal retrieval due to its fast query speed and low storage cost. However, existing methods often fail to fully explore the semantic correlations among different categories, resulting in less discriminative learned hash codes. In this paper, we propose a deep cross-modal hashing method named hierarchical semantic structure preserving hashing (HSSPH), which directly exploits the label hierarchy information to learn discriminative hash codes.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Xiumei Wang, Tianmeng Li, Zheng Hui, Peitao Cheng
Summary: This paper proposes a deep learning based stereo image super-resolution algorithm that utilizes additional information in stereo image pairs to enhance the quality of reconstructed images. The authors introduce an adaptive modulation alignment mechanism and the use of rectangular convolution kernel to address the challenges caused by occlusion. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on multiple stereo benchmarks.
PATTERN RECOGNITION LETTERS
(2022)
Article
Automation & Control Systems
Di Wang, Songwei Han, Quan Wang, Lihuo He, Yumin Tian, Xinbo Gao
Summary: The study proposes a pseudo-label guided collective matrix factorization (PLCMF) method, which jointly learns latent representations and cluster structures. PLCMF first performs clustering on each view separately, then adds a pseudo-label constraint on collective matrix factorization, and finally integrates latent representation learning and cluster structure learning into a joint framework to directly obtain clustering results.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yumin Tian, Aqiang Ding, Di Wang, Xuemei Luo, Bo Wan, Yifeng Wang
Summary: Image-text matching is a hot research topic, but existing methods fail to fully exploit detailed correlations between images and texts. To address this, a method called Bi-Attention Enhanced Representation Learning (BAERL) is proposed, which utilizes self-attention and co-attention learning sub-networks to capture intra-modality and inter-modality correlations. BAERL also uses the self-similarity polynomial loss for training, improving retrieval performance. Experimental results on benchmark datasets demonstrate the superiority of BAERL over state-of-the-art methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Di Wang, Aqiang Ding, Yumin Tian, Quan Wang, Lihuo He, Xinbo Gao
Summary: Multimodal metric learning is important for computing cross-modal similarity in heterogeneous data. However, existing methods are not effective for hierarchical labeled data. In this study, we propose a deep hierarchical multimodal metric learning (DHMML) method that can obtain hierarchical discriminative modality-invariant representations for multimodal data. We demonstrate the superiority of DHMML over several state-of-the-art methods through experiments on benchmark datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Di Wang, Shuai Liu, Quan Wang, Yumin Tian, Lihuo He, Xinbo Gao
Summary: Multimodal sentiment analysis (MSA) is important in various applications and language modality is crucial for recognition accuracy. This paper proposes a Cross-modal Enhancement Network (CENet) that integrates visual and acoustic information into a language model to enhance text representations. Experimental results demonstrate the superiority of CENet over existing methods on benchmark datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Geochemistry & Geophysics
Ke Li, Di Wang, Xu Wang, Gang Liu, Zili Wu, Quan Wang
Summary: This article proposes a unified framework called mixing self-attention and convolution network (MACN) for multisource remote sensing data fusion. The framework utilizes adaptive CNN encoders to extract shallow convolutional features and achieves local and global multiscale perception through an elegant integration of self-attention and convolution. A multisource cross-guided fusion module is also designed for deep fusion. Experimental results show that the proposed method outperforms other models and achieves state-of-the-art results on multiple RS data fusion tasks.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Xuemei Luo, Xiaotong Luo, Di Wang, Jinhui Liu, Bo Wan, Lin Zhao
Summary: Video captioning is a hot research topic that aims to describe the content of a video in accurate and fluent natural language. Existing methods often overlook the importance of frames and the semantic correlations between videos and texts. The proposed GSEN framework addresses these issues by utilizing a feature aggregation module and a global semantic enhancement module to generate high-quality captions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Jingyuan Yang, Jie Li, Leida Li, Xiumei Wang, Yuxuan Ding, Xinbo Gao
Summary: This study addresses the issue of subjectivity in visual emotion analysis and proposes a novel method to tackle this problem. By simulating the emotion evocation process and incorporating an attention mechanism, the proposed method is able to better predict people's emotions towards different visual stimuli.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zheng Hui, Jie Li, Xiumei Wang, Xinbo Gao
Summary: This study introduces an adaptive modulation network (AMNet) for blind super-resolution (SR) with multiple degradations and incorporates deep reinforcement learning into the entire blind SR model to address non-differentiable issues.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
