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Attribute community detection based on latent representation learning and graph regularized non-negative matrix factorization

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Shang, Ronghua Zhang, Weitong Li, Zhiyuan Wang, Chao Jiao, Licheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Res Ctr Big Data Intelligence Zhejiang Lab Hangzhou 311100 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Guangdong Peoples R China

In recent years, complex networks have become increasingly difficult to detect effectively with the rapid development of networks. On the one hand, the scale of the network has increased sharply, and on the other hand, the nodes in the network contain rich content. The existing algorithms do not take the different richness of attribute information of nodes in the attribute network into account. And the topology information of the node is inconsistent with the attribute information. Thus, this paper proposes an attribute community detection algorithm based on latent representation learning and graph-regularized non-negative matrix factorization (LRL-GNMF). First, the topological information and attribute information in the attribute network is decomposed based on non-negative matrix factorization respectively. Thereby, the member distribution matrix and the attribute distribution matrix are obtained. Secondly, an affinity matrix is constructed for the attribute matrix, and the latent representation of the attribute information is obtained using the latent representation learning method. In addition, a transition matrix is constructed according to the Markov transition probability. The node membership distribution matrix and the attribute distribution matrix are linked. Finally, since the attribute information of nodes in the network is different, a topology-dominated model and an attribute-dominated model are respectively constructed to solve this problem. At the same time, a graph regularization term is introduced to guide the model to obtain more accurate community detection. This paper conducts experimental analysis on 8 real networks. The experimental results show that the proposed algorithm in this paper outperforms the other 11 compared algorithms in the evaluation indicators of community detection accuracy and standard mutual information. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Community detection non -negative matrix factorization Latent representation learning Transition matrix Attribute network

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Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

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GEOTHERMICS 2022年 106卷

作者： Vesselinov, V. V. Ahmmed, B. Mudunuru, M. K. Pepin, J. D. Burns, E. R. Siler, D. L. Karra, S. Middleton, R. S. EnviTrace LLC Santa Fe NM 87501 USA Los Alamos Natl Lab Earth & Environm Sci Div Los Alamos NM 87545 USA Pacific Northwest Natl Lab Watershed & Ecosyst Sci Richland WA 99352 USA US Geol Survey Albuquerque NM 87113 USA US Geol Survey Portland OR 97201 USA US Geol Survey Moffett CA 94035 USA Pacific Northwest Natl Lab Environm Mol Sci Lab Richland WA 99352 USA Carbon Solut LLC Okemos MI 48864 USA

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low-and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these sig-natures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (< 90 degrees C) and medium (90-150 degrees C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Dati

关键词： Geothermal energy Unsupervised machine learning non -negative matrix factorization Custom k -means clustering Feature extraction Hidden signatures Hidden geothermal resources

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Feature nonlinear Transformation non-negative matrix factorization with Kullback-Leibler Divergence

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PATTERN RECOGNITION 2022年 132卷

作者： Hu, Lirui Wu, Ning Li, Xiao Beibu Gulf Univ Coll Elect & Informat Engn Qinzhou 535000 Guangxi Peoples R China Key Lab Big Data Resources Utilizat Qinzhou 535000 Guangxi Peoples R China Key Lab Adv Technol Internet Things Qinzhou 535000 Guangxi Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin 541000 Guangxi Peoples R China

This paper introduces a Feature nonlinear Transformation non-negative matrix factorization with Kullback-Leibler Divergence (FNTNMF-KLD) for extracting the nonlinear features of a matrix in standard NMF. This method uses a nonlinear transformation to act on the feature matrix for constructing a NMF model based on the objective function of Kullback-Leibler Divergence, and the Taylor series expansion and the Newton iteration formula of solving root are used to obtain the iterative update rules of the basis ma-trix and the feature matrix. Experimental results show that the proposed method obtains the nonlinear features of data matrix in a more efficient way. In object recognition and clustering tasks, better accuracy can be achieved over some typical NMF methods.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： non -negative matrix factorization nonlinear transformation Feature extraction Object recognition Clustering Kullback-Leibler divergence

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Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge

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MEDICAL IMAGE ANALYSIS 2022年 77卷 102378页

作者： Won, Ji Hye Youn, Jinyoung Park, Hyunjin Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea Sungkyunkwan Univ Samsung Med Ctr Sch Med Dept Neurol Seoul South Korea Neurosci Ctr Samsung Med Ctr Seoul South Korea Sungkyunkwan Univ Sch Elect & Elect Engn Suwon South Korea

Neuroimaging genetics is a powerful approach to jointly explore genetic features with rich brain imaging phenotypes for neurodegenerative diseases. Conventional imaging genetics approaches based on canonical correlation analysis cannot accommodate multimodal inputs effectively and have limited interpretabil-ity. We propose a novel imaging genetics approach based on non-negative matrix factorization (NMF). By leveraging the parsimonious property known as topic modeling in multi-view NMF, we add sparsity constraints and prior information to identify a sparse set of biologically related features across modali-ties. Thus, our approach incorporates prior knowledge and improves multimodal integration capabilities and interpretability. We applied our algorithm to simulated and real imaging genetics datasets of Parkin-son's disease (PD) for performance evaluation. Our algorithm could identify important associated features mapped to interpretable distinct topics more robustly than other methods. It revealed promising features of single-nucleotide polymorphisms and brain regions related to a subset of PD-related clinical scores in a few topics using a real imaging genetic dataset. The proposed imaging genetics approach can re-veal novel associations between genetic and neuroimaging features to improve understanding of various neurodegenerative diseases. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Neuroimaging genetics non -negative matrix factorization Topic modeling Prior knowledge Multimodal integration Interpretability Parkinson's diseases

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nondestructive 3D imaging and quantification of hydrated biofilm matrix by confocal Raman microscopy coupled with non-negative matrix factorization

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WATER RESEARCH 2022年 210卷 117973页

作者： Liu, Xiao-Yang Guo, Shuxia Bocklitz, Thomas Roesch, Petra Popp, Juergen Yu, Han-Qing Univ Sci & Technol China Dept Environm Sci & Engn CAS Key Lab Urban Pollutant Convers Hefei 230026 Peoples R China Hebei Univ Technol Sch Energy & Environm Engn Tianjin 300130 Peoples R China Friedrich Schiller Univ Jena Inst Phys Chem Helmholtzweg 4 D-07743 Jena Germany Friedrich Schiller Univ Jena Abbe Ctr Photon Helmholtzweg 4 D-07743 Jena Germany InfectoGnost Res Campus Jena Philosophenweg 7 D-07743 Jena Germany Jena Univ Hosp Ctr Sepsis Control & Care CSCC Klinikum 1 D-07743 Jena Germany Leibniz Inst Photon Technol Jena Res Alliance Leibniz Hlth Technol Albert Einstein Str 9 D-07745 Jena Germany

Biofilms are ubiquitous in natural and engineered environments and of great importance in drinking water distribution and biological wastewater treatment systems. Simultaneously acquiring the chemical and structural information of the hydrated biofilm matrix is essential for the cognition and regulation of biofilms in the environmental field. However, the complexity of samples and the limited approaches prevent a holistic understanding of the biofilm matrix. In this work, an approach based on the confocal Raman mapping technique integrated with non-negative matrix factorization (NMF) analysis was developed to probe the hydrated biofilm matrix in situ. The flexibility of the NMF analysis was utilized to subtract the undesired water background signal and resolve the meaningful biological components from Raman spectra of the hydrated biofilms. Diverse chemical components such as proteins, bacterial cells, glycolipids and polyhydroxyalkanoates (PHA) were unraveled within the distinct Pseudomonas spp. biofilm matrices, and the corresponding 3-dimensional spatial organization was visualized and quantified. Of these components, glycolipids and PHA were unique to the P. aeruginosa and P. putida biofilm matrix, respectively. Furthermore, their high abundances in the lower region of the biofilm matrix were found to be related to the specific physiological functions and surrounding microenvironments. Overall, the results demonstrate that our NMF Raman mapping method could serve as a powerful tool complementary to the conventional approaches for identifying and visualizing the chemical components in the biofilm matrix. This work may facilitate the online characterization of the biofilm matrix widely present in the environment and advance the fundamental understanding of biofilm.

关键词： Biofilm Raman mapping non -negative matrix factorization Pseudomonas putida Pseudomonas aeruginosa

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RRNMF-MAGL: Robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning for dimensionality reduction

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INFORMATION SCIENCES 2023年 640卷

作者： Yi, Yugen Lai, Shumin Li, Shicheng Dai, Jiangyan Wang, Wenle Wang, Jianzhong Jiangxi Normal Univ Sch Software Nanchang 330022 Peoples R China Northeast Normal Univ Coll Informat Sci & Technol Changchun 130117 Peoples R China Weifang Univ Sch Comp Engn Weifang 261061 Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Peoples R China

In this paper, a new unsupervised dimensionality reduction method named robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning (RRNMF-MAGL) is developed. Compared with the existing methods, RRNMF-MAGL can extract robust lowdimensional features and adaptively learn a manifold structure to well reflect the data distribution. Specifically, to alleviate the influence of outliers and noises, a robust non-negative matrix factorization (RNMF) approach with L21-norm loss function is first constructed. Then, a multiconstraint adaptive graph learning (MAGL) model is designed based on low-dimensional features to reduce the computational complexity and avoid the adverse influence of redundant information. Moreover, multiple constraints (i.e., sparsity and locality) are incorporated into graph learning for enhancing the discrimination ability of graph structure. Next, to make the learned low-dimensional features well maintain the local geometric structure information of data, a graph Laplacian regularization (GLR) is designed. Finally, an iterative update strategy is proposed to optimize RRNMF-MAGL and its convergence is verified by numerical experiments. The superior effectiveness and robustness of RRNMF-MAGL is demonstrated by extensive experiments on eight publicly available datasets.

关键词： non -negative matrix factorization Adaptive graph learning Sparsity and locality constraint L 21-norm Graph regularization

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Mask-guided cycle-GAN for specular highlight removal

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PATTERN RECOGNITION LETTERS 2022年 161卷 108-114页

作者： Hu, Guangwei Zheng, Yuanfeng Yan, Haoran Hua, Guang Yan, Yuchen Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China

Specular highlight removal is an important yet challenging problem in image enhancement. Recent meth-ods based on deep learning and trained by massive paired or unpaired data have demonstrated promising performance for this task. Methods based on unpaired data have recently gained more attention for eas-ier training data collection. In this paper, we present a Mask-Guided Cycle-GAN for specular highlight removal on unpaired data. Incorporating the idea that specular highlight mainly has characteristics in lightness, we attempt to train a module only on luminance channel before considering all channels, and then adopt the training results to guide the subsequent highlight removal module. We further convert the highlight removal problem to image-to-image translation by using cycle-consistent adversarial net-work (Cycle-GAN). In the proposed network, a non-negative matrix factorization (NMF) based method is incorporated to obtain accurate highlight masks. The proposed method is evaluated using the specu-lar highlight image quadruples (SHIQ) and the LIME datasets, and the advantages are demonstrated via comparative experimental results.(c) 2022 Published by Elsevier B.V.

关键词： Specular highlight removal non -negative matrix factorization Unpaired data Cycle-GAN

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One step multi-view spectral clustering via joint adaptive graph learning and matrix factorization

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NEUROCOMPUTING 2023年 524卷 95-105页

作者： Yang, Wenqi Wang, Yansu Tang, Chang Tong, Hengjian Wei, Ao Wu, Xia China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Peoples R China Tianjin Chest Hosp Dept Cardiol Tianjin 300222 Peoples R China Nanjing Med Univ Lianshui Peoples Hosp Kangda Coll Nursing Dept Huaian 223300 Peoples R China

Multi-view clustering based on graph learning has attracted extensive attention due to its simplicity and efficiency in recent years. However, there are still some issues in most of the existing graph-based multi-view clustering methods. First, most of those methods require post-processing such as K-means or spec-tral rotation to get the final discrete clustering result. Second, graph-based clustering methods perform clustering on a fixed input similarity graph, which could induce bad clustering results if the initial graph is with low quality. Third, these methods have high computation cost, which hinders them for dealing with large-scale data. In order to solve these problems, we propose a multi-view spectral clustering method via joint Adaptive Graph Learning and matrix factorization (AGLMF). In this method, to reduce computational cost, we adopt the anchor-based strategy to construct the input similarity graphs. Then, we use the l1-norm to learn a high quality similarity graph adaptively from original similarity graphs which can make the final graph more robust than original ones. In addition, AGLMF uses symmetric non-negative matrix factorization to learn the final clustering indicators which can show the final consis-tent clustering result directly. Finally, experimental results on multiple multi-view datasets validate the effectiveness of the proposed algorithm when compared with previous multi-view spectral clustering algorithms. The demo code of this work is publicly available at https://***/theywq/***.(c) 2022 Published by Elsevier B.V.

关键词： Multi-view clustering Spectral clustering Adaptive graph learning non -negative matrix factorization

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Adaptive graph regularization and self-expression for noise-aware feature selection

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NEUROCOMPUTING 2023年 535卷 107-122页

作者： Shang, Ronghua Chi, Haijing Li, Yangyang Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

Many traditional unsupervised feature selection algorithms utilize manifold information to mine the local structure of the data. However, the noise existing in the raw data reduces the accuracy of the man-ifold information of data, which affects the learning effect of the entire algorithm. In order to solve the above problems and more fully find the internal structure inside the data, this paper proposes an adaptive graph regularization and self-expression for noise-aware feature selection (ASNFS). Firstly, the algorithm adopts non-negative matrix factorization to decompose the raw data matrix, and utilizes the low-dimensional matrix generated after decomposition to replace the raw high-dimensional data matrix. This allows the algorithm to reveal some internal structural information of the raw data while reducing the dimensionality of the data. ASNFS also introduces the orthogonal basis clustering with excellent clus-tering effect, and the interpretability of the algorithm is enhanced. Secondly, in addition to preserving the manifold information in the low-dimensional projection subspace, the algorithm also preserves the man-ifold information in the non-negative matrix factorization subspace. Meanwhile, the adaptive graph reg-ularization term added to the objective function enables the algorithm to continuously update the similarity matrix. It can effectively remove the noise inside the raw data, and prevent the occurrence of over-fitting phenomenon of the experimental results caused by the fixed similarity matrix. Finally, the similarity matrix retains the local structure information of the data with each iteration, and the results of the feature selection are reused for the construction of the similarity matrix. ASNFS adopts an alternate iterative method to optimize the objective function, which is simple and effective. Then, the algorithm complexity and convergence are analyzed. ASNFS is compared with seven feature selection algorithms on nine datasets, and the e

关键词： non -negative matrix factorization Orthogonal basis clustering Adaptive graph regularization Unsupervised feature selection

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Advanced transient noise reduction in speech signals via semi-supervised signal fusion

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APPLIED ACOUSTICS 2024年 218卷

作者： Ullah, Rizwan Zhang, Shaohui Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

Speech enhancement (SE) by transient noise suppression refers to the process of estimating the desired signal from a transient noise (TN)-corrupted speech signal. It has important applications in speaker verification and identification, voice-based biometric systems, hearing aids, video conferences, and many others. Enhancement of TN-corrupted signals is both important and challenging due to their high randomness, high short-time energy, and wide frequency domain distribution. This paper presents a novel approach to SE that leverages the power of sequential sparse nonnegative matrix factorization (SNMF), enhanced dictionary learning (DL), and Gini index (GI)-based fusion. We use a multi-step approach to enhance noisy speech. Our approach uses semi-supervised learning since the TN used for dictionary training is derived from the noisy signal using optimally-modified log-spectral amplitude (OMLSA) and is then purified. We involve both the dictionaries of the noisy signal and external clean speech in the DL process to merge the strengths of both dictionaries. Firstly, the dictionary of the noisy signal is obtained through SNMF and is then decomposed into speech-dominant and noise-dominant submatrices using semi-supervised learning. Then this speech-dominant submatrix is combined with the clean speech dictionary to construct an improved speech dictionary. This enhanced dictionary and the external clean speech dictionary are then used for the SE in the testing phase, resulting in two different initial estimated signals. To improve the accuracy of the initial estimates, we apply the GI to obtain the final estimate. Furthermore, the noisy signal's phase is also enhanced. A significant increase is observed in the results through different evaluation measures.

关键词： Speech enhancement non -negative matrix factorization Dictionary learning Gini index Signal fusion

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