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第40届中国控制会议

作者： Sigan Pan Lei Yang School of Mechatronic Engineering and Automation Shanghai University

nonnegative matrix factorization（NMF） is a widely used clustering method, which has been successfully applied to image clustering, community detection, text clustering, and other applications. However, NMF uses the linear combination of basis vectors, so when there is a nonlinear structure, NMF cannot find the combination of basis vectors representing the one cluster. Meanwhile, higher-order information among the nodes and the limited label information is often ignored. In this paper,we present a new model, namely NMF with hypergraph based on discriminative constraint and nonsymmetric reformulation（DNHNMF） to address this shortcoming. Specifically, DNHNMF formulates a symmetric matrix containing pairwise similarity values by reformulating the nonsymmetric matrix, explicitly combining label information and higher-order information, excavate the potential inherent cluster structure, and making the Euclidean distance between coefficient matrices（clustering assignment matrix） better represent the similarity measure among nodes. The clustering experiments of the proposed method are carried out and validated on eight standard datasets. Extensive experimental results show the effectiveness of the DNHNMF.

关键词： nonnegative matrix factorization Image clustering Community detection Hypergraph regularization Nonsymmetric reformulation

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Sparse Separable nonnegative matrix factorization

Sparse Separable Nonnegative Matrix Factorization

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Nadisic, Nicolas Vandaele, Arnaud Cohen, Jeremy E. Gillis, Nicolas Univ Mons Mons Belgium Univ Rennes IRISA CNRS INRIA Rennes France

ISBN: (纸本)9783030676582;9783030676575

We propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor are equal to columns of the input matrix, while sparsity requires that the columns of the second NMF factor are sparse. We call this variant sparse separable NMF (SSNMF), which we prove to be NP-complete, as opposed to separable NMF which can be solved in polynomial time. The main motivation to consider this new model is to handle underdetermined blind source separation problems, such as multispectral image unmixing. We introduce an algorithm to solve SSNMF, based on the successive nonnegative projection algorithm (SNPA, an effective algorithm for separable NMF), and an exact sparse nonnegative least squares solver. We prove that, in noiseless settings and under mild assumptions, our algorithm recovers the true underlying sources. This is illustrated by experiments on synthetic data sets and the unmixing of a multispectral image.

关键词： nonnegative matrix factorization Sparsity Separability

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Learning Scanning Regime For Electronic Support Receivers by nonnegative matrix factorization 44

Learning Scanning Regime For Electronic Support Receivers by...

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44th International Conference on Telecommunications and Signal Processing (TSP)

作者： Gul, Ismail Erer, Isin Istanbul Tech Univ Elect & Commun Dept Istanbul Turkey ASELSAN AS Radar Elect Warfare & Intelligence Syst Div Ankara Turkey

ISBN: (纸本)9781665429337

Narrow-band receivers used in electronic support systems should operate with a frequency scanning strategy in order to detect radar signals in different frequency ranges of the electromagnetic spectrum. This scanning strategy can be determined with learning-based models in an environment where the parameters of the radars are unrecognized. In previous studies, the problem is modeled as a dynamic system with Predictive State Representations and the resulting optimization problem is solved via Singular Value Thresholding (SVT) algorithm. We propose a scanning regime learning method based on nonnegative matrix factorization (NMF) algorithm. The proposed method requires less computation time for subspace identification in each iteration. According to the simulation results, the average calculation time is reduced around 40% by using NMF without any loss of detection performance.

关键词： Electronic Support Measures Electronic Support Receivers Frequency Search Strategy nonnegative matrix factorization Signal Intercept

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Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality

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CHAOS SOLITONS & FRACTALS 2022年 159卷

作者： Lv, Laishui Bardou, Dalal Hu, Peng Liu, Yanqiu Yu, Gaohang China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Univ Abbes Laghrour Dept Comp Sci & Math ICOSI Lab Khenchela Algeria Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Peoples R China Hangzhou Dianzi Univ Coll Sci Hangzhou 310000 Peoples R China

Temporal link prediction aims to predict new links at time T + 1 based on a given temporal network from time 1 to T. Some existing temporal link prediction methods only consider the naturally links direction formation but ignore the local and global information of temporal networks. To address this issue, in this paper, we propose a novel graph regularized nonnegative matrix factorization algorithm for the problem of temporal link prediction by using PageRank centrality and asymmetric link clustering coefficient, referred to as the GNMFCA. Specifically, we use graph regularization to capture the local information of each slice in temporal networks and PageRank centrality to compute the importance of nodes in each slice, which captures the global information of each slice in temporal networks. By jointly optimizing them in a nonnegative matrix factorization model, the proposed model can preserve both the local and global information simultaneously. Besides, we propose effective multiplicative updating rules to solve the proposed model and provide the convergence analysis of the iterative algorithm. Finally, we perform numerical experiments on eight artificial and four real-world temporal networks to demonstrate that GNMFCA outperforms some existing temporal link prediction algorithms. (c) 2022 Elsevier Ltd.

关键词： Temporal link prediction Temporal networks Graph regularization nonnegative matrix factorization PageRank centrality

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A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and nonnegative matrix factorization

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REMOTE SENSING 2022年第4期14卷 1042页

作者： Ren, Zhongliang Zhai, Qiuping Sun, Lin Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Linyi Univ Sch Resource & Environm Sci Shandong Prov Key Lab Water & Soil Conservat & En Linyi 276000 Shandong Peoples R China

The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects.

关键词： hyperspectral mineral mapping clustering spectral matching nonnegative matrix factorization

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Hybrid Feature Measurement based on Linear and Nonlinear nonnegative matrix factorization 22

Hybrid Feature Measurement based on Linear and Nonlinear Non...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Sicong Ye Yang Zhao Jihong Pei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering in Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450398336

The nonnegative matrix factorization algorithm is an effective data dimensionality reduction method. The principle is to convert the image into a nonnegative linear combination of low dimensional basis images. nonnegative matrix factorization can be divided into linear algorithm and nonlinear algorithm. Because of different decomposition theory, linear NMF algorithms mainly extract first-order features of data, while nonlinear NMF algorithms mainly extract high-order features. Most of the current studies only focus on one of the models without combining the two together, which leads to the lack of data features. Therefore it is necessary to integrate the two types of algorithms for research. The paper proposes hybrid feature measurement based on linear and nonlinear nonnegative matrix factorization. The algorithm utilizes the idea of feature fusion. The basis image features of the two algorithms are mixed in the model. Finally a feature similarity measurement is obtained as the measure method. The proposed algorithm has good performance on the public datasets and effectively improves the recognition.

关键词： Hybrid feature measurement nonnegative matrix factorization Basis image

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A Novel Enhanced nonnegative matrix factorization Method for Face Recognition

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第3期36卷

作者： Chen, Wen-Sheng Chen, Haitao Pan, Binbin Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

nonnegative matrix factorization (NMF), distinguished from the approaches for holistic feature representation, is able to acquire meaningful basis images for parts-based representation. However, NMF does not utilize the data-label information and usually achieves undesirable performance in classification. To address the above-mentioned problem of NMF, this paper proposes a new enhanced NMF (ENMF) method for facial image representation and recognition. We seek to learn powerfully discriminative feature by a label-based regularizer which describes the relationship between the data. It is desired that minimizing the regularizer makes the data from the same class have high similarity and the data from different classes have low similarity. This good property will contribute to improving the performance of NMF. Therefore, we propose an objective function of ENMF by incorporating the label-based regularizer into the loss function. Subsequently, we find the stationary point of the constructed auxiliary function by means of Cardano's formula and derive the update rules of our ENMF algorithm. The convergence of the proposed ENMF is both theoretically sound and empirically validated. Finally, the proposed ENMF method is successfully applied to face recognition. Four publicly available face datasets, namely AR, Caltech 101, Yale, and CMU PIE, are chosen for evaluations. Compared with the state-of-the-art NMF-based algorithms, the experimental results illustrate that the proposed ENMF algorithm achieves superior performance.

关键词： nonnegative matrix factorization face recognition supervised learning

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Secured Secret Sharing of QR Codes Based on nonnegative matrix factorization and Regularized Super Resolution Convolutional Neural Network

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SENSORS 2022年第8期22卷 2959页

作者： Velumani, Ramesh Sudalaimuthu, Hariharasitaraman Choudhary, Gaurav Bama, Srinivasan Jose, Maranthiran Victor Dragoni, Nicola Inst Elect & Elect Engineers IEEE Aruppukottai 626101 India VIT Bhopal Univ Sch Comp Sci & Engn SCSE Bhopal 466114 India Tech Univ Denmark DTU DTU Compute DK-2800 Lyngby Denmark Kalasalingam Acad Res & Educ Krishnankovil Srivilliputtur 626128 India Noorul Islam Ctr Higher Educ Kumaracoil Thucklay 673012 Kanyakumari India

Advances in information technology have harnessed the application of Quick Response (QR) codes in day-to-day activities, simplifying information exchange. QR codes are witnessed almost everywhere, on consumables, newspapers, information bulletins, etc. The simplicity of QR code creation and ease of scanning with free software have tremendously influenced their wide usage, and since QR codes place information on an object they are a tool for the IoT. Many healthcare IoT applications are deployed with QR codes for data-labeling and quick transfer of clinical data for rapid diagnosis. However, these codes can be duplicated and tampered with easily, attributed to open- source QR code generators and scanners. This paper presents a novel (n,n) secret-sharing scheme based on nonnegative matrix factorization (NMF) for secured transfer of QR codes as multiple shares and their reconstruction with a regularized Super Resolution Convolutional Neural Network (SRCNN). This scheme is an alternative to the existing polynomial and visual cryptography-based schemes, exploiting NMF in part-based data representation and structural regularized SRCNN to capture the structural elements of the QR code in the super-resolved image. The experimental results and theoretical analyses show that the proposed method is a potential solution for secured exchange of QR codes with different error correction levels. The security of the proposed approach is evaluated with the difficulty in launching security attacks to recover and decode the secret QR code. The experimental results show that an adversary must try 2(58) additional combinations of shares and perform 3 x 2(88) additional computations, compared to a representative approach, to compromise the proposed system.

关键词： secret sharing quick response code nonnegative matrix factorization super resolution convolutional neural network structural regularization basis matrix coefficient matrix

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Hidden Markov model in nonnegative matrix factorization for fetal heart rate estimation using physiological priors

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PHYSIOLOGICAL MEASUREMENT 2022年第10期43卷 105001-105001页

作者： Rojas, Mariel Resendiz Fontecave-Jallon, Julie Rivet, Bertrand Univ Grenoble Alpes CNRS UMR 5525 VetAgro SupGrenoble INPTIMC F-38000 Grenoble France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

Objective. Fetal heart rate (fHR) analysis remains the most common technique for detecting fetal distress when monitoring the fetal well-being during labor. If cardiotocography (CTG) is nowadays the non-invasive clinical reference technique for fHR measurement, it suffers from several drawbacks, hence an increasing interest towards alternative technologies, especially around abdominal ECG (aECG). Approach. An original solution, using a single abdominal lead, was recently proposed to address both the feasibility in clinical routine and the challenging detection of temporal events when facing interfered signals from real life conditions. Based on a specification of the non-negative matrix factorization (NMF) algorithm, it exploits the semi-periodicity of fetal electrocardiogram (fECG) for fHR estimation. However, this method assumes temporal independence and therefore does not consider the continuity property of fHR values. It is thus proposed to add to the NMF framework a hidden Markov model (HMM) to include physiological information about fHR temporal evolution. Under a statistical setting, constraints have been added by accommodating regularization terms through Bayesian priors. Main results. The proposed method is evaluated on 23 real aECG signals from a new clinical database, according to CTG reference, and compared with the original NMF-only algorithm. The new proposed method improves performance, with an agreement with CTG increasing from 71% to 80%. Significance. This highlights the interest of a better modelization of the fHR characteristics for a more robust estimation.

关键词： fetal monitoring fetal ECG fetal heart rate nonnegative matrix factorization hidden Markov model

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Unsupervised EEG channel selection based on nonnegative matrix factorization

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期76卷 000-000页

作者： Xu, Lingfeng Chavez-Echeagaray, Maria Elena Berisha, Visar Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

High-density Electroencephalogram (EEG) systems have proven to be useful in enhancing the performance of emotion recognition algorithms. However, the high-dimensional nature of this data modality may also result in irrelevant information being captured, causing overfitting problems and increasing the computational cost of downstream algorithms. To perform efficient and accurate emotion recognition, an unsupervised channel selection framework based on semi-nonnegative matrix factorization (semi-NMF) is proposed. The algorithm excels in analyzing signals with complex internal correlations and produces results that are easy to interpret. Semi-NMF was used to decompose the high-density EEG signal matrices into several activation patterns. The strongest activation pattern was considered as most related to emotion recognition and channels with large weights in that activation pattern were selected for valence-based emotion recognition. It was found that the proposed framework can effectively detect brain regions that were active during emotional activities, and, using only this reduced set of channels, achieve better recognition performance than using all channels. Compared to existing methods, the framework selects channels in a physiologically explainable way and requires no supervised feature engineering or class labels. It results in higher accuracy compared to other unsupervised energy-based methods, and on par with the supervised ReliefF method. In all, the proposed framework not only serves as a valid channel selection tool for practical emotion recognition, but also has the possibility to be transferred to other non-classification tasks, potentially contributing to a variety of EEG applications, such as brain state monitoring, pathological brain activation analysis and brain disease diagnosis.

关键词： EEG signal Emotion recognition nonnegative matrix factorization Channel selection Feature extraction

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