Article
Engineering, Electrical & Electronic
Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong, Qingfu Zhang
Summary: This paper introduces a self-supervised symmetric nonnegative matrix factorization (SNMF) method to improve data clustering performance. By exploiting the sensitivity to initialization of SNMF, without relying on additional information, the method progressively enhances clustering results. Experimental results demonstrate its superiority over 14 state-of-the-art methods in terms of multiple quantitative metrics.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Zhenzhu Chen, Anmin Fu, Robert H. Deng, Ximeng Liu, Yang Yang, Yinghui Zhang
Summary: The dimensionality reduction aims at reducing redundant information in big data for efficient data analysis. Enterprises or individuals with limited resources often outsource this task to the cloud. However, privacy and security concerns arise due to inadequate supervision. A proposed scheme based on incremental NMF method addresses these concerns while ensuring data confidentiality and verifiability of computation results. Experiment evaluation shows the scheme's high efficiency in saving over 80% computation time for clients.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Analytical
Yinsheng Zhang, Ling Jin, Fangjie Guo, Xiaofeng Ni, Yaju Zhao, Yongbo Cheng, Haiyan Wang
Summary: This paper provides a theoretical reformulation of relevant dimensionality reduction algorithms under a unified matrix factorization perspective and develops an open-sourced toolkit to integrate these algorithms. A comparative study on matrix factorization-based dimensionality reduction algorithms is conducted, and guidelines for algorithm selection are summarized based on the case study results.
ANALYTICAL CHEMISTRY
(2022)
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, Artificial Intelligence
Ali Hassani, Amir Iranmanesh, Najme Mansouri
Summary: This study introduces a new feature agglomeration method based on nonnegative matrix factorization and proposes a deterministic initialization method for spherical K-means algorithm, which significantly improves the stability and performance of text data clustering.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Haoran Chen, Xu Chen, Hongwei Tao, Zuhe Li, Xiao Wang
Summary: This article proposes a novel low-rank representation model, which achieves data clustering through adaptive dimensionality reduction and manifold optimization. Experimental results demonstrate the significant advantages of this method.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Hardware & Architecture
Jiangzhang Gan, Tong Liu, Li Li, Jilian Zhang
Summary: The paper introduces the application and advantages of Non-negative matrix factorization (NMF) in the field of data science, provides a detailed analysis of existing methods, discusses various variants of NMF, and evaluates the performance of nine NMF methods through experiments.
Article
Computer Science, Information Systems
Guoqiu Wen, Xianxian Li, Yonghua Zhu, Linjun Chen, Qimin Luo, Malong Tan
Summary: This paper proposes a one-step spectral rotation clustering method OSRCIH, which integrates self-paced learning and spectral rotation clustering in a unified framework to simultaneously consider sample selection and dimensionality reduction. Experimental analysis shows that OSRCIH can effectively recognize important samples and features in imbalanced high-dimensional data, improving clustering performance.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Neurosciences
Shengchao Zhang, Sarah E. Goodale, Benjamin P. Gold, Victoria L. Morgan, Dario J. Englot, Catie Chang
Summary: Patterns in fMRI data can reflect dynamic changes in the brain and are related to individual and group differences in behavior, cognition, and clinical traits. Detecting vigilance states in fMRI data without external measurements is challenging. This study shows that vigilance levels can be detected in the low-dimensional structure of fMRI data, even within individual time frames.
Article
Computer Science, Artificial Intelligence
Qi Wang, Xiang He, Xu Jiang, Xuelong Li
Summary: Data clustering has attracted much attention, with various effective algorithms developed to handle the task. Non-negative matrix factorization (NMF) is considered powerful, but it has limitations in terms of sensitivity to noise and outliers. Existing graph-based NMF methods highly depend on the initial similarity graph and perform graph construction and matrix factorization separately, leading to suboptimal graph structures.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yong Peng, Keding Chen, Feiping Nie, Bao-Liang Lu, Wanzeng Kong
Summary: This study proposes a novel Fuzzy k-means (FKM) method called two-dimensional embedded fuzzy data clustering (2DEFC), which retains structural information and optimizes the projection matrices of two subspaces collaboratively. By optimizing the input and clustering processes for 2D data, competitive performance in 2D data clustering is achieved.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jiao Wei, Can Tong, Bingxue Wu, Qiang He, Shouliang Qi, Yudong Yao, Yueyang Teng
Summary: This article presents a new type of nonnegative matrix factorization (NMF) called entropy weighted NMF (EWNMF), which assigns an optimizable weight to each attribute of each data point to emphasize their importance. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hongyuan Zhang, Yanan Zhu, Xuelong Li
Summary: This study proposes a novel projected clustering framework to capture the essence of deep clustering by summarizing the core properties of powerful models, especially deep models. The framework introduces an aggregated mapping, consisting of projection learning and neighbor estimation, to obtain clustering-friendly representation. The study also addresses the problem of severe degeneration in simple clustering-friendly representation learning, and develops a self-evolution mechanism to alleviate the risk of over-fitting.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Biology
Farshad Saberi-Movahed, Mahyar Mohammadifard, Adel Mehrpooya, Mohammad Rezaei-Ravari, Kamal Berahmand, Mehrdad Rostami, Saeed Karami, Mohammad Najafzadeh, Davood Hajinezhad, Mina Jamshidi, Farshid Abedi, Mahtab Mohammadifard, Elnaz Farbod, Farinaz Safavi, Mohammadreza Dorvash, Negar Mottaghi-Dastjerdi, Shahrzad Vahedi, Mahdi Eftekhari, Farid Saberi-Movahed, Hamid Alinejad-Rokny, Shahab S. Band, Iman Tavassoly
Summary: Establishing an intelligent triage system is a critical challenge in managing complex diseases like COVID-19. This article presents a machine learning approach that predicts poor prognosis and morbidity in COVID-19 patients by analyzing blood test results, paving the way for quantitative and optimized clinical management systems.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Shiming He, Meng Guo, Zhuozhou Li, Ying Lei, Siyuan Zhou, Kun Xie, Neal N. Xiong
Summary: KPI clustering is important for AIOps when dealing with a large number of KPIs. This approach divides KPIs into classes and applies the same model to detect anomalies or predict outcomes for the KPIs in each class, reducing computational overhead. However, irregular KPIs caused by varying sampling strategies have not been fully addressed. This study proposes an iterative clustering scheme based on matrix factorization to solve the problem of clustering irregular KPIs and achieves higher NMI compared to non-iterative clustering.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Yuanjun Huang, Xianbin Cao, Qi Wang, Baochang Zhang, Xiantong Zhen, Xuelong Li
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Yuwu Lu, Chun Yuan, Xuelong Li, Zhihui Lai, David Zhang, Linlin Shen
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Yanwei Pang, Li Ye, Xuelong Li, Jing Pan
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2018)
Article
Computer Science, Artificial Intelligence
Tianji Pang, Feiping Nie, Junwei Han, Xuelong Li
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2019)
Review
Biology
Chao Gao, Chen Liu, Daniel Schenz, Xuelong Li, Zili Zhang, Marko Jusup, Zhen Wang, Madeleine Beekman, Toshiyuki Nakagaki
PHYSICS OF LIFE REVIEWS
(2019)
Article
Computer Science, Artificial Intelligence
Rui Zhang, Feiping Nie, Xuelong Li
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Automation & Control Systems
Bin Wang, Xiuying Yuan, Xinbo Gao, Xuelong Li, Dacheng Tao
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Automation & Control Systems
Yuwu Lu, Zhihui Lai, Xuelong Li, Wai Keung Wong, Chun Yuan, David Zhang
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Automation & Control Systems
Yi Bin, Yang Yang, Fumin Shen, Ning Xie, Heng Tao Shen, Xuelong Li
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Geochemistry & Geophysics
Qi Wang, Zhenghang Yuan, Qian Du, Xuelong Li
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2019)
Article
Geochemistry & Geophysics
Qi Wang, Shaoteng Liu, Jocelyn Chanussot, Xuelong Li
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2019)
Article
Computer Science, Information Systems
Di Hu, Feiping Nie, Xuelong Li
IEEE TRANSACTIONS ON MULTIMEDIA
(2019)
Article
Computer Science, Artificial Intelligence
Jingkuan Song, Yuyu Guo, Lianli Gao, Xuelong Li, Alan Hanjalic, Heng Tao Shen
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Qi Wang, Zequn Qin, Feiping Nie, Xuelong Li
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Xiaozhao Fang, Na Han, Wai Keung Wong, Shaohua Teng, Jigang Wu, Shengli Xie, Xuelong Li
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)