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Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2021年第SUPPL 5期21卷 95-95页

作者： Ding, Menghan Luo, Yuan Northwestern Univ Feinberg Sch Med Dept Prevent Med Chicago IL 60611 USA

Background Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care. Methods Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications. Results We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characteri

关键词： Sepsis Phenotyping Physiological measurements Intensive care unit Unsupervised learning Clustering nonnegative matrix factorization Frequent subgraph mining Gradient boosting machine

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Community detection based on nonnegative matrix tri-factorization for multiplex social networks (Publication with Expression of Concern. See vol. 13, 2025)

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JOURNAL OF COMPLEX NETWORKS 2024年第2期12卷 cnae012-cnae012页

作者： Zhang, Jun Wang, Fenfen Zhou, Jian Hunan Railway Profess Technol Coll Sch Artificial Intelligence Zhuzhou 412001 Hunan Peoples R China Hunan Railway Profess Technol Coll Finance Dept Zhuzhou 412001 Hunan Peoples R China

Nowadays, community detection is one of the important fields for understanding network topology and has many applications in information diffusion, interaction mining and migration behaviour analysis. Therefore, community detection in social networks can help to understand user behaviour and network characteristics. There are many community detection methods, which are often designed for single-layer social networks. However, real-world networks use several types of relationships to establish connections between users, each of which has different characteristics. Hence, real-world networks can be modelled as multiplex networks. In general, multiplex networks are an example of multilayer networks in which the relationships between users in different networks can be considered simultaneously. In these networks, each layer represents the connections between users in a social network. Meanwhile, communities in multiplex networks are identified based on the structure and connections between overlapping users in different layers of the network. In this article, the nonnegative matrix tri-factorization (NMTF) strategy is used to model multiplex social networks, and a solution for community detection is developed based on it. According to this strategy, a common consensus matrix and then an alignment matrix are extracted based on similarity metrics and network structure. The use of these matrices as a flexible modelling framework enables the detection of coherent community between overlapping users in multiplex social networks. We evaluate the proposed NMTF method through various metrics on a multiplex social network. The results of this evaluation show the better performance of NMTF in terms of community quality compared to equivalent methods.

关键词： multiplex networks community detection nonnegative matrix factorization consensus matrix

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Diverse joint nonnegative matrix tri-factorization for attributed graph clustering

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APPLIED SOFT COMPUTING 2024年 164卷

作者： Mohammadi, Arina Seyedi, Seyed Amjad Tab, Fardin Akhlaghian Mohammadiani, Rojiar Pir Univ Kurdistan Dept Comp Engn Sanandaj Iran

Cluster analysis of attributed graphs is a demanding and challenging task in the analysis of network-structured data. It involves learning node representation by leveraging both node attributes and the topological structure of the graph, aiming to accomplish effective clustering. Typically, existing methods fuse the topological and non-topological information by learning a consensus representation, often resulting in redundancy and overlooking their inherent distinctions. To address this issue, this paper proposes the Diverse Joint nonnegative matrix Tri-factorization (Div-JNMTF), an embedding based model to detect communities in attributed graphs. The novel JNMTF model attempts to extract two distinct node representations from topological and non-topological data. Simultaneously, a diversity regularization technique utilizing the Hilbert-Schmidt Independence Criterion (HSIC) is employed. Its objective is to reduce redundant information in the node representations while encouraging the distinct contributions of both types of information. In addition, two graph regularization terms are introduced to preserve the local structures in the topological and attribute representation spaces. The Div-JNMTF model is optimized by developing an iterative optimization approach. By conducting thorough experiments on four synthetic and eight real-world attributed graph datasets, it has been demonstrated that the proposed model excels in accurately detecting attributed communities and surpasses the performance of existing methods.

关键词： Attributed graph clustering nonnegative matrix factorization Hilbert-Schmidt independence criterion Graph regularization

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Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction

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PATTERN RECOGNITION LETTERS 2019年 122卷 53-59页

作者： Atif, Syed Muhammad Qazi, Sameer Gillis, Nicolas PAF Karachi Inst Econ & Technol Grad Sch Sci & Engn Karachi Pakistan Univ Mons Fac Polytech Dept Math & Operat Res Rue Houdain 9 B-7000 Mons Belgium

Due to the iterative nature of the most nonnegative matrix factorization (NMF) algorithms, initialization is a key aspect as it significantly influences both the convergence and the final solution obtained. Many initialization schemes have been proposed for NMF, among which one of the most popular class of methods are based on the singular value decomposition (SVD) and clustering. However, these SVD-based initializations as well as clustering based initializations (if they dense their right factor H), do not satisfy a rather natural condition, namely that the error should decrease as the rank of factorization increases. In this paper, we propose a novel SVD-based NMF initialization to specifically address this shortcoming by taking into account the SVD factors that were discarded to obtain a nonnegative initialization. This method, referred to as nonnegative SVD with low-rank correction (NNSVD-LRC), allows us to significantly reduce the initial error at a negligible additional computational cost using the low-rank structure of the discarded SVD factors. NNSVD-LRC has two other advantages compared to other NMF initializations: (1) it provably generates sparse initial factors, and (2) it is faster as it only requires to compute a truncated SVD of rank left perpendicular r/2 + 1 right perpendicular where r is the factorization rank of the sought NMF decomposition (as opposed to a rank-r truncated SVD for other methods). We show on several standard dense and sparse data sets that our new method competes favorably with state-of-the-art SVD-based and clustering based initializations for NMF. (C) 2019 Elsevier B.V. All rights reserved.

关键词： nonnegative matrix factorization Initialization Singular value decomposition Clustering based NMF initialization CR1-NMF

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Regularized asymmetric nonnegative matrix factorization for clustering in directed networks

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PATTERN RECOGNITION LETTERS 2019年 125卷 750-757页

作者： Tosyali, Ali Kim, Jinho Choi, Jeongsub Jeong, Myong K. Univ Delaware Dept Accounting & Management Informat Syst Newark DE 19716 USA Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA

In network analysis, clustering is a key task of dividing a network into logical and meaningful groupings of components. There are various methods to cluster nodes in undirected networks, however, little is known about clustering in directed networks. In this paper, we propose a regularized asymmetric nonnegative matrix factorization (RANMF) algorithm for clustering in directed networks. In a given directed network, the RANMF exploits the pairwise similarity of nodes to make close nodes belong to the same cluster under the guidance of prior information of the network. We also prove the convergence of the RANMF algorithm and provide real-world experiments to show its performance. The experimental results show the superiority of our RANMF algorithm in terms of several clustering validity indices. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Clustering Directed network nonnegative matrix factorization

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Adaptive Wiener Gain to Improve Sound Quality on nonnegative matrix factorization-Based Noise Reduction System

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IEEE ACCESS 2019年 7卷 43286-43297页

作者： Lai, Ying-Hui Wang, Syu-Siang Chen, Chien-Hsun Jhang, Sin-Hua Natl Yang Ming Univ Dept Biomed Engn Taipei 112 Taiwan MOST Joint Res Ctr AI Technol & All Vista Healthcare Taipei 106 Taiwan Natl Taiwan Normal Univ Dept Mechatron Engn Taipei 106 Taiwan

nonnegative matrix factorization (NMF) is a useful decomposition technique for multivariate data. More recently, NMF technology was used as a noise-estimation stage for a Wiener-filtering-based noise reduction (NR) method to improve the quality of noisy speech. Previous studies showed that this method provides better sound quality performance than conventional NMF-based approaches;however, there is still scope for improving the performance under noisy listening conditions. More specifically, the performance of an NMF noise estimator for calculating the noise level is considered sensitive to diverse noise environments and signal-to-noise ratio conditions. Therefore, we proposed an adaptive algorithm that derives an adaptive factor (a) to adjust the weight between the estimated speech and noise levels on the basis of the signal-to-noise level for the gain function of the Wiener-filtering-based NR method to further improve the sound quality. Two objective evaluations and listening tests evaluated the benefits of the proposed method, and experimental results show that better output sound quality and competed for speech intelligibility performance can be achieved when compared with conventional unsupervised NR and NMF-based methods.

关键词： Noise reduction nonnegative matrix factorization

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A convergent algorithm for bi-orthogonal nonnegative matrix tri-factorization

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2021年第4期15卷 1069-1102页

作者： Mirzal, Andri King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran Saudi Arabia

A convergent algorithm for nonnegative matrix factorization (NMF) with orthogonality constraints imposed on both basis and coefficient matrices is proposed in this paper. This factorization concept was first introduced by Ding et al. (Proceedings of 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 126-135, 2006) with intent to further improve clustering capability of NMF. However, as the original algorithm was developed based on multiplicative update rules, the convergence of the algorithm cannot be guaranteed. In this paper, we utilize the technique presented in our previous work Mirzal (J Comput Appl Math 260:149-166, 2014a;Proceedings of the first international conference on advanced data and information engineering (DaEng-2013). Springer, pp 177-184, 2014b;IEEE/ACM Trans Comput Biol Bioinform 11(6):1208-1217, 2014c) to develop a convergent algorithm for this problem and prove that it converges to a stationary point inside the solution space. As it is very hard to numerically show the convergence of an NMF algorithm due to the slow convergence and numerical precision issues, experiments are instead performed to evaluate whether the algorithm has the nonincreasing property (a necessary condition for the convergence) where it is shown that the algorithm has this property. Further, clustering capability of the algorithm is also inspected by using Reuters-21578 data corpus.

关键词： Clustering methods Convergent algorithm nonnegative matrix factorization Orthogonality constraint

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Overlapping community detection via Layer-Jaccard similarity incorporated nonnegative matrix factorization

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NEURAL NETWORKS 2025年 189卷 107601页

作者： Zhuo, Zhijian Chen, Bilian Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis m Xiamen 361005 Peoples R China

As information modernization progresses, the connections between entities become more elaborate, forming more intricate networks. Consequently, the emphasis on community detection has transitioned from discerning disjoint communities towards the identification of overlapping communities. A variety of algorithms based on the sparse adjacency matrix, which are sensitive to edge connections, are suitable for detecting edge-sparse areas between overlapping communities but lack the ability to detect edge-dense areas within the overlapping communities. Additionally, most algorithms do not take into account multihop information. To mitigate the aforementioned limitations, we propose an innovative approach termed Layer-Jaccard similarity incorporated nonnegative matrix factorization (LJSINMF), which utilizes both the adjacency matrix and the Layer-Jaccard similarity matrix. Our method initially employs a newly proposed Onion-shell method to decompose the network into layers. Subsequently, the layer of each node is used to construct a Layer-Jaccard similarity matrix, which facilitates the identification of edge-dense areas within the overlapping communities and serves as a general approach for enhancing other nonnegative matrix factorization-based algorithms. Ultimately, we integrate the adjacency matrix and the Layer-Jaccard similarity matrix into the nonnegative matrix factorization framework to determine the node-community membership matrix. Moreover, integrating the Layer-Jaccard similarity matrix into other algorithms is a promising approach to enhance their performance. Comprehensive experiments have been conducted on real-world networks and the results substantiate that the LJSINMF algorithm outperforms most state-of-the-art baseline methods in terms of three evaluation metrics.

关键词： Community detection Overlapping communities Graph shell Similarity nonnegative matrix factorization

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Cloud removal in remote sensing images using nonnegative matrix factorization and error correction

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2019年第Feb.期148卷 103-113页

作者： Li, Xinghua Wang, Liyuan Cheng, Qing Wu, Penghai Gan, Wenxia Fang, Lina Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China CCCC Second Highway Consultants Co Ltd Wuhan 430056 Hubei Peoples R China Wuhan Univ Sch Urban Design Wuhan 430070 Hubei Peoples R China Anhui Univ Sch Resources & Environm Engn Hefei 230601 Anhui Peoples R China Wuhan Inst Technol Sch Resource & Civil Engn Wuhan 430205 Hubei Peoples R China Fuzhou Univ Spatial Informat Res Ctr Fujian Prov Fuzhou 350002 Fujian Peoples R China

In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier to the effective observation of sensors. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix factorization (NMF) and error correction method (S-NMF-EC) is proposed in this paper. Firstly, a cloud-free fused reference image is obtained by a reference image and two or more low-resolution images using the spatial and temporal nonlocal filter-based data fusion model (STNLFFM). Secondly, the cloud-free fused reference image is used to remove the cloud cover of the cloud-contaminated image based on NMF. Finally, the cloud removal result is further improved by error correction. It is worth noting that cloud detection is not required by S-NMF-EC, and the cloud-free information of the cloud-contaminated image is maximally retained. Both simulated and real-data experiments were conducted to validate the proposed S-NMF-EC method. Compared with other cloud removal methods, the results demonstrate that S-NMF-EC is visually and quantitatively effective (correlation coefficients >= 0.99) for the removal of thick clouds, thin clouds, and shadows.

关键词： Cloud removal Error correction nonnegative matrix factorization Multitemporal Optical remote sensing image

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Adaptive Graph Regularization Discriminant nonnegative matrix factorization for Data Representation

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IEEE ACCESS 2019年 7卷 112756-112766页

作者： Zhang, Lin Liu, Zhonghua Wang, Lin Pu, Jiexin Henan Univ Sci & Technol Informat Engn Coll Luoyang 471000 Peoples R China

nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields. However, the traditional nonnegative matrix factorization directly factoring decomposes the original data, and the original data often contains a lot of redundancy and noise, which seriously affect the subsequent processing of the data. In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering. The AGDNMF algorithm makes full use of local structure information and a small amount of label information. In AGDNMF, the local structure information can be more accurate and the label information can prevent the points with the same label from being merged into one point. These two items are combined into the objective function of NMF. In addition, we provide the update rules for the corresponding optimization functions and prove its convergence. A large number of experiments on different data sets show that the proposed algorithm has good clustering performance.

关键词： nonnegative matrix factorization graph regularization discriminative information image clustering data representation

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