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检索条件"主题词=Nonnegative Matrix factorization"

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MITIGATION OF ULTRA WIDE-BAND INTERFERENCE FOR SAR USING nonnegative matrix factorization WITH PRIOR CONSTRAINTS 38

MITIGATION OF ULTRA WIDE-BAND INTERFERENCE FOR SAR USING NON...

引用

38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tao, Mingliang Zhang, Xinyu Su, Jia Zhang, Zijing Northwestern Polytech Univ Inst Res & Dev Shenzhen Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781538671504

The proliferation of radio technology increase the likelihood of interference to active remote sensing systems, especially for those high-resolution synthetic aperture radar (SAR) systems with large bandwidth. The presence of radio frequency interference (RFI) in SAR data would affect the image quality and subsequent image interpretation results. Nowadays, radio services have an increasing demand for greater bandwidth, and the contamination bandwidth by RFI are becoming wider. This paper discusses the extreme case that SAR echoes are contaminated by ultra wide-band RFI, i.e., the bandwidth of RFI is relatively larger than the transmitted signal, traditional methods would fail due to large signal loss. In this paper, we proposed a mitigation method using nonnegative matrix factorization with prior constraints like independence and sparsity. The experimental results indicate the effectiveness of the proposed method.

关键词： Synthetic aperture radar radio frequency interference interference mitigation nonnegative matrix factorization

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Clustering Stability via Concept-based nonnegative matrix factorization

Clustering Stability via Concept-based Nonnegative Matrix Fa...

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作者： Nghia Duong-Trung Minh-Hoang Nguyen Hanh T.H.Nguyen Can Tho University of Technology FPT University Department of Information Engineering and Computer Science University of Trento

One of the most important contributions of topic modeling is to accurately and the ectively discover and classify documents in a collection of texts by a number of clusters/topics. However, finding an appropriate number of topics is a particularly challenging model selection question. In this context, we introduce a new unsupervised conceptual stability framework to access the validity of a clustering solution. We integrate the proposed framework into nonnegative matrix factorization(NMF) to guide the selection of desired number of topics. Our model provides a exible way to enhance the interpretation of NMF for the effective clustering solutions. The work presented in this paper crosses the bridge between stability-based validation of clustering solutions and NMF in the context of unsupervised learning. We perform a thorough evaluation of our approach over a wide range of real-world datasets and compare it to current state-of-the-art which are two NMF-based approaches and four Latent Dirichlet Allocation(LDA) based models. the quantitative experimental results show that integrating such conceptual stability analysis into NMF can lead to significant improvements in the document clustering and information retrieval the ectiveness.

关键词： Concept Discovery Conceptual Stability Topic Modeling Unsupervised Document Clustering nonnegative matrix factorization

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Privacy-Preserving POI Recommendation Using nonnegative matrix factorization 2

Privacy-Preserving POI Recommendation Using Nonnegative Matr...

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2nd IEEE Symposium on Privacy-Aware Computing (PAC)

作者： Wang, Xiwei Yang, Hao Lim, Kiho NE Illinois Univ Departmnet Comp Sci Chicago IL 60625 USA Yancheng Teachers Univ Sch Informat Engn Yancheng Peoples R China

ISBN: (纸本)9781538684429

Location based social networks (LBSNs) have become an essential part of life for many smartphone users. With the sheer volume of new information in LBSNs produced every day, people can easily feel overwhelmed when deciding which places to visit, e.g., restaurants, grocery stores, bars. Point-of-interest (POI) recommender systems are there to help people find their favorite places. To make recommendations, the system needs to learn users' preference, which usually requires their check-in data. This can potentially deter people from using the system because personal location and check-in data are considered as users' privacy and many do not feel comfortable sharing the information with other parties. In this paper, we propose a non-negative matrix factorization (NMF) based privacy-preserving POI recommendation framework, in which the latent factors in NMF are learned on user group preference instead of individual user preference. Recommendations are made by personalizing the group preference on user's local devices. There are no location or check-in data collected from the users anywhere throughout the learning and recommendation processes.

关键词： point-of-interest recommendation nonnegative matrix factorization privacy

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Neurodynamics-Based nonnegative matrix factorization for Classification 25th

Neurodynamics-Based Nonnegative Matrix Factorization for Cla...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Zhang, Nian Leatham, Keenan Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA

ISBN: (纸本)9783030041793;9783030041786

This paper contributes to study the influence of various NMF algorithms on the classification accuracy of each classifier as well as to compare the classifiers among themselves. We focus on a fast nonnegative matrix factorization (NMF) algorithm based on a discrete-time projection neural network (DTPNN). The NMF algorithm is combined with three classifiers in order to find out the influence of dimensionality reduction performed by the NMF algorithm on the accuracy rate of the classifiers. The convergent objective function values in terms of two popular objective functions, Frobenius norm and Kullback-Leibler (K-L) divergence for different NMF based algorithms on a wide range of data sets are demonstrated. The CPU running time in terms of these objective functions on different combination of NMF algorithms and data sets are also shown. Moreover, the convergent behaviors of different NMF methods are illustrated. In order to test its effectiveness on classification accuracy, a performance study of three well-known classifiers is carried out and the influence of the NMF algorithm on the accuracy is evaluated.

关键词： nonnegative matrix factorization Discrete-time projection neural network Dimensional reduction Feature selection Classification

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STOCHASTIC VARIANCE REDUCED MULTIPLICATIVE UPDATE FOR nonnegative matrix factorization

STOCHASTIC VARIANCE REDUCED MULTIPLICATIVE UPDATE FOR NONNEG...

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

作者： Kasai, Hiroyuki Univ Electrocommun Tokyo Japan

ISBN: (纸本)9781538646588

nonnegative matrix factorization (NMF), a dimensionality reduction and factor analysis method, is a special case in which factor matrices have low-rank nonnegative constraints. Considering the stochastic learning in NMF, we specifically address the multiplicative update (MU) rule, which is the most popular, but which has slow convergence property. This present paper introduces on the stochastic MU rule a variance-reduced technique of stochastic gradient. Numerical comparisons suggest that our proposed algorithms robustly outperform state-of-the-art algorithms across different synthetic and real-world datasets.

关键词： nonnegative matrix factorization multiplicative update stochastic gradient variance reduction

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Incremental nonnegative matrix factorization with Sparseness Constraint for Image Representation 19th

Incremental Nonnegative Matrix Factorization with Sparseness...

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19th Pacific-Rim Conference on Multimedia (PCM)

作者： Sun, Jing Wang, Zhihui Li, Haojie Sun, Fuming Dalian Univ Technol Dalian 116300 Peoples R China Liaoning Univ Technol Jinzhou 121001 Peoples R China

ISBN: (纸本)9783030007676;9783030007669

nonnegative matrix factorization (NMF) is a powerful method of data dimension reduction and has been widely used in face recognition. However, existing NMF algorithms have two main drawbacks. One is that the speed is too slow for large matrix factorization. The other is that it must conduct repetitive learning when the training samples or classes are incremental. In order to overcome these two limitations and improve the sparseness of the data after factorization, this paper presents a novel algorithm, which is called incremental nonnegative matrix factorization with sparseness constraint. By using the results of previous factorization involved in iterative computation with sparseness constraint, the cost of computation is reduced and the sparseness of data after factorization is greatly improved. Compared with NMF and INMF, the experimental results on some face databases have shown that the proposed method achieves superior results.

关键词： nonnegative matrix factorization Incremental Sparseness constraint

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Class-Cone based nonnegative matrix factorization for Face Recognition 14

Class-Cone based Nonnegative Matrix Factorization for Face R...

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14th International Conference on Computational Intelligence and Security (CIS)

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

ISBN: (纸本)9781728101699

nonnegative matrix factorization (NMF) is an effectively parts-based feature representation approach and has achieved good performance in different tasks such as computer vision, clustering and so on. To enhance the discriminative power of NMF in nonnegative feature space, this paper proposes a novel supervised matrix decomposition method, called Class-Cone based nonnegative matrix factorization (CCNMF). We establish a loss function with class-cone regularization which contains the volumes of class-cones and the quantity of between class-cones. To minimize the objective function will leads to small class-cones and large distance between class cones. This good property is beneficial to the performance of NMF algorithm. We solve the optimization problem using KKT conditions and obtain the updating rules of CCNMF. Our approach is experimentally shown to be convergence and successfully applied to face recognition. Experimental results demonstrate the effectiveness of the proposed CCNMF algorithm.

关键词： Feature Extraction nonnegative matrix factorization Face Recognition

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Multi-task Feature Learning for EEG-based Emotion Recognition Using Group nonnegative matrix factorization 26

Multi-task Feature Learning for EEG-based Emotion Recognitio...

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European Signal Processing Conference (EUSIPCO)

作者： Hajlaoui, Ayoub Chetouani, Mohamed Essid, Slim Univ Paris 06 Inst Syst Intelligents & Robot Paris France Univ Paris Saclay Telecom ParisTech LTCI Paris France

ISBN: (纸本)9789082797015

Electroencephalographic sensors have proven to be promising for emotion recognition. Our study focuses on the recognition of valence and arousal levels using such sensors. Usually, ad hoc features are extracted for such recognition tasks. In this paper, we rely on automatic feature learning techniques instead. Our main contribution is the use of Group nonnegative matrix factorization in a multi-task fashion, where we exploit both valence and arousal labels to control valence-related and arousal-related feature learning. Applying this method on HCI MAHNOB and EMOEEG, two databases where emotions are elicited by means of audiovisual stimuli and performing binary inter-session classification of valence labels, we obtain significant improvement of valence classification F1 scores in comparison to baseline frequency-band power features computed on predefined frequency bands. The valence classification F1 score is improved from 0.56 to 0.69 in the case of HCI MAHNOB, and from 0.56 to 0.59 in the case of EMOEEG.

关键词： Electroencephalography Valence Arousal nonnegative matrix factorization Group NMF Common Spectral Patterns

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Event Detection Based on nonnegative matrix factorization: Ceasefire Violation, Environmental, and Malware Events

Event Detection Based on Nonnegative Matrix Factorization: C...

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AHFE International Conference on Human Factors in Cybersecurity

作者： Drake, Barry Huang, Tiffany Beavers, Ashley Du, Rundong Park, Haesun Georgia Inst Technol 75 5TH St NWSTE 900 Atlanta GA 30308 USA

ISBN: (纸本)9783319605852;9783319605845

Event detection is a very important problem across many domains and is a broadly applicable encompassing many disciplines within engineering systems. In this paper, we focus on improving the user's ability to quickly identify threat events such as malware, military policy violations, and natural environmental disasters. The information to perform these detections is extracted from text data sets in the latter two cases. Malware threats are important as they compromise computer system integrity and potentially allow the collection of sensitive information. Military policy violations such as ceasefire policies are important to monitor as they disrupt the daily lives of many people within countries that are torn apart by social violence or civil war. The threat of environmental disasters takes many forms and is an ever-present danger worldwide, and indiscriminate regarding who is harmed or killed. In this paper, we address all three of these threat event types using the same underlying technology for mining the information that leads to detecting such events. We approach malware event detection as a binary classification problem, i.e., one class for the threat mode and another for non-threat mode. We extend our novel classifier utilizing constrained low rank approximation as the core algorithm innovation and apply our Non-negative Generalized Moody-Darken Architecture (NGMDA) hybrid method using various combinations of input and output layer algorithms. The new algorithm uses a nonconvex optimization problem via the nonnegative matrix factorization (NMF) for the hidden layer of a single layer perceptron and a nonnegative constrained adaptive filter for the output layer estimator. We first show the utility of the core NMF technology for both ceasefire violation and environmental disaster event detection. Next NGMDA is applied to the problem of malware threat events, again based on the NMF as the core computational tool. Also, we demonstrate that an algorithm should be a

关键词： Malware detection Event detection Perceptron Clustering nonnegative matrix factorization Adaptive filtering Hybrid classifier Topic modeling Classification

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Image Completion with nonnegative matrix factorization Under Separability Assumption 1

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14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA)

作者： Sadowski, Tomasz Zdunek, Rafal Wroclaw Univ Sci & Technol Fac Elect Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (数字)9783319937649

ISBN: (纸本)9783319937649;9783319937632

nonnegative matrix factorization is a well-known unsupervised learning method for part-based feature extraction and dimensionality reduction of a nonnegative matrix with a variety of applications. One of them is a matrix completion problem in which missing entries in an observed matrix is recovered on the basis of partially known entries. In this study, we present a geometric approach to the low-rank image completion problem with separable nonnegative matrix factorization of an incomplete data. The proposed method recursively selects extreme rays of a simplicial cone spanned by an observed image and updates the latent factors with the hierarchical alternating least-squares algorithm. The numerical experiments performed on several images with missing entries demonstrate that the proposed method outperforms other algorithms in terms of computational time and accuracy.

关键词： nonnegative matrix factorization Image completion Geometric NMF Separable matrix factorization

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