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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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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

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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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 76卷

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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Log-based sparse nonnegative matrix factorization for data representation

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Peng, Chong Zhang, Yiqun Chen, Yongyong Kang, Zhao Chen, Chenglizhao Cheng, Qiang Qingdao Univ Coll Comp Sci & Technol Qingdao Shandong Peoples R China Harbin Inst Technol Dept Comp Sci Harbin Heilongjiang Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Univ Kentucky Dept Comp Sci Lexington KY USA Univ Kentucky Inst Biomed Informat Lexington KY USA

nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness. Moreover, we propose a novel column-wisely sparse norm, named l(2,log)-(pseudo) norm to enhance the robustness of the proposed method. The l(2,log)-(pseudo) norm is invariant, continuous, and differentiable. For the l(2,log) regularized shrinkage problem, we derive a closed-form solution, which can be used for other general problems. Efficient multiplicative updating rules are developed for the optimization, which theoretically guarantees the convergence of the objective value sequence. Extensive experimental results confirm the effectiveness of the proposed method, as well as the enhanced sparseness and robustness. (C) 2022 Elsevier B.V. All rights reserved.

关键词： nonnegative matrix factorization Sparse Robust Convergence

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nonnegative matrix factorization with Fixed L2-Norm Constraint

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2019年第7期38卷 3211-3226页

作者： Yang, Zuyuan Hu, Yifei Liang, Naiyao Lv, Jun Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China

nonnegative matrix factorization (NMF) is a very attractive scheme in learning data representation, and constrained NMF further improves its ability. In this paper, we focus on the L2-norm constraint due to its wide applications in face recognition, hyperspectral unmixing, and so on. A new algorithm of NMF with fixed L2-norm constraint is proposed by using the Lagrange multiplier scheme. In our method, we derive the involved Lagrange multiplier and learning rate which are hard to tune. As a result, our method can preserve the constraint exactly during the iteration. Simulations in both computer-generated data and real-world data show the performance of our algorithm.

关键词： nonnegative matrix factorization Multiplicative updates L2-norm Constrained NMF

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RANDOM PROJECTION STREAMS FOR (WEIGHTED) nonnegative matrix factorization

RANDOM PROJECTION STREAMS FOR (WEIGHTED) NONNEGATIVE MATRIX ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yahaya, Farouk Puigt, Matthieu Delmaire, Gilles Roussel, Gilles Univ Littoral Cote dOpale LISIC EA 4491 F-62228 Calais France

ISBN: (纸本)9781728176055

Random projections recently became popular tools to process big data. When applied to nonnegative matrix factorization (NMF), it was shown that, in practice, with the same compression level, structured random projections were more efficient than classical strategies based on, e.g., Gaussian compression. However, as they are data-dependent, they remain costly and might not fully benefit from recent very fast random projection techniques. In this paper, we thus investigate an alternative framework to structured random projections-named random projection streams (RPS)-which (i) are based on classical random compression strategies only-and are thus data-independent-and (ii) can benefit from the above fast techniques. We experimentally show that, under some mild conditions, RPS allow the same NMF performance as structured random projection along iterations. We also show that even a CPU implementation of Gaussian Compression Streams allows a faster convergence than structured random projections when applied to weighted NMF.

关键词： Random projections Big data nonnegative matrix factorization Weighted nonnegative matrix factorization Compressive learning

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nonnegative matrix factorization for clustering ensemble based on dark knowledge

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KNOWLEDGE-BASED SYSTEMS 2019年 163卷 624-631页

作者： Ye, Wenting Wang, Hongjun Yan, Shan Li, Tianrui Yang, Yan Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 60031 Sichuan Peoples R China

Traditional cluster ensemble (CE) methods use labels produced by base learning algorithms to obtain an ensemble result. These base learning algorithms can also obtain other information, such as parameter, covariance, or probability data, which is called dark knowledge. In this paper, we propose a method for integrating dark knowledge, which is usually ignored, into the ensemble learning process. This provides more information about the base clustering. We apply nonnegative matrix factorization (NMF) to the clustering ensemble model based on dark knowledge. First, different base clustering results are obtained by using various clustering configurations, before dark knowledge of every base clustering algorithm is extracted. NMF is then applied to the dark knowledge to obtain integrated results. Experimental results show that the method outperforms other clustering ensemble techniques. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cluster ensemble nonnegative matrix factorization Dark knowledge

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