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检索条件"主题词=Non-negative tensor factorization"

共 65 条记录，以下是31-40 订阅

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White Matter Fiber-Bundle Analysis Using non-negative tensor factorization 13th

White Matter Fiber-Bundle Analysis Using Non-negative Tensor...

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13th International Conference on Image Analysis and Recognition in Memory of Mohamed Kamel (ICIAR)

作者： Stamile, Claudio Cotton, Francois Maes, Frederik Sappey-Marinier, Dominique Van Huffel, Sabine Univ Lyon 1 INSA Lyon INSERM U1044 CREATISCNRS UMR5220 Lyon France Katholieke Univ Leuven STADIUS Dept Elect Engn ESAT Leuven Belgium Katholieke Univ Leuven PSI Dept Elect Engn ESAT Leuven Belgium Ctr Hosp Lyon Sud Hosp Civils Lyon Serv Radiol Pierre Benite France Univ Lyon CERMEP Imagerie Vivant Bron France iMinds Med Informat Technol Leuven Belgium

ISBN: (纸本)9783319415017;9783319415000

With the development of advanced image acquisition and processing techniques providing better biomarkers for the characterization of brain diseases, the automatic analysis of biomedical imaging constitutes a critical point. In particular, analysis of complex data structure is a challenge for better understanding complex brain pathologies like multiple sclerosis (MS). In this work, we describe a new fully automated method based on non-negative tensor factorization (NTF) to analyze white matter (WM) fiber-bundles. This method allows to extract, from a WM fiber-bundle, the set of fibers affected by the pathology, discriminating fibers affected by the pathological from the healthy fibers. Our method was validated on simulated data and also applied on real MS patients. Results show the high precision level of our method to extract fibers affected by the pathological process.

关键词： non-negative tensor factorization White matter Multiple sclerosis DTI Tractography

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AN ADAPTIVE TIME-FREQUENCY RESOLUTION FRAMEWORK FOR SINGLE CHANNEL SOURCE SEPARATION BASED ON non-negative tensor factorization

AN ADAPTIVE TIME-FREQUENCY RESOLUTION FRAMEWORK FOR SINGLE C...

引用

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

作者： Kirbiz, S. Gunsel, B. Istanbul Tech Univ Multimedia Signal Proc & Pattern Recognit Grp Dept Elect & Comm Eng Istanbul Turkey

ISBN: (纸本)9781479903566

In this paper, we propose an adaptive timefrequency resolution based single channel sound source separation method using nonnegative tensor factorization (NTF). The model aims to alleviate drawbacks of working by fixed length Short Time Fourier Transform (STFT) by minimizing the smearing of signal energy in both time and frequency. A joint optimization scheme has been applied based on KLdivergence where each layer of the tensor represents the mixture at a different resolution. In order to enclose sparseness into factorization, the resynthesis is made through an adaptive weighted fusion procedure which combines the separated sources in a manner that maximizes the energy concentration. Test results reported over a large sound database indicate the introduced NTF based fusion method improves the sound quality both in terms of conventional and perceptual distortion measures.

关键词： non-negative tensor factorization sound source separation adaptive time-frequency resolution

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MULTICHANNEL BLIND SOURCE SEPARATION BASED ON non-negative tensor factorization IN WAVENUMBER DOMAIN 41

MULTICHANNEL BLIND SOURCE SEPARATION BASED ON NON-NEGATIVE T...

引用

41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Mitsufuji, Yuki Koyama, Shoichi Saruwatari, Hiroshi Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Sony Corp Audio Technol Dev Dept Tokyo Japan

ISBN: (纸本)9781479999880

Multichannel non-negative matrix factorization based on a spatial covariance model is one of the most promising techniques for blind source separation. However, this approach is not tractable for a large number of microphones, M, because the computational cost is of order O(M-3) per time-frequency bin. To circumvent this drawback, we propose non-negative tensor factorization in the wavenumber domain, which reduces the cost to the order O(M). It transforms microphone signals into the spatial frequency domain, a technique that is commonly used for soundfield reconstruction. The proposed method is compared to several blind source separation (BSS) methods in terms of separation quality and computational cost.

关键词： Multichannel BSS non-negative tensor factorization Wavenumber Domain DoA Spatial Covariance Model

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CitySpectrum: A non-negative tensor factorization Approach 14

CitySpectrum: A Non-negative Tensor Factorization Approach

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ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

作者： Fan, Zipei Song, Xuan Shibasaki, Ryosuke Univ Tokyo Ctr Spatial Informat Sci Tokyo Japan

ISBN: (纸本)9781450329682

People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space (CitySpectral Space). To obtain the CitySpectrum, we utilized non-negative tensor factorization (NTF) to decompose a people flow tensor into basic life pattern tensors, described by three bases i.e. the intensity variation among different regions, the time-of-day and the sample days. We apply our approach to a big mobile phone GPS log dataset (containing 1.6 million users) to model the fluctuation in people flow before and after the Great East Japan Earthquake from a CitySpectral perspective. In addition, our framework is extensible to a variety of auxiliary spatial-temporal data. We parametrize a people flow with a spatial distribution of the Points of Interest (POIs) to quantitatively analyze the relationship between human mobility and POI distribution. Based on the parametric people flow, we propose a spectral approach for a site-selection recommendation and people flow simulation in another similar area using POI distribution.

关键词： Human Mobility non-negative tensor factorization

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Controller Performance Assessment for Switched Linear Systems via Applying non-negative tensor factorization 34

Controller Performance Assessment for Switched Linear System...

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34th Chinese Control Conference (CCC)

作者： Jiang, Deng-Yin Hu, Li-Sheng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9789881563897

This paper considers the controller performance assessment problem based on the data-driven benchmark by applying the tensor approach for switched systems. For each subprocess, the multivariate output with finite sample numbers is extracted regarding as the forms of feature matrices. Consequently, by stacking the feature matrices associated to any data set samplings, a tensor is created, a fact which necessitates studying the controller performance assessment for switched systems considered using tensors. In order to fit the multiple invariance of the measurement output tensor data processing for controller performance assessment, the non-negative tensor factorization model is proposed to analyze the tensor data, which stems from uniqueness of low-rank decomposition of higher-order tensor. By using the non-negative tensor factorization, the estimated latent outputs structure combining with the covariance-based algorithm are given to derive the data-driven performance benchmark and performance index for each subprocess controller, and the presented data-driven benchmark estimation algorithm requires a set of close-loop routine operating data. A simulation example is provided to test the effectiveness and advantages of proposed method.

关键词： Controller performance assessment Switched linear systems Data-driven benchmark tensor non-negative tensor factorization

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Contaminant Monitoring for Industrial Emission-Diffusion Process based on non-negative tensor factorization 29

Contaminant Monitoring for Industrial Emission-Diffusion Pro...

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第29届中国控制与决策会议

作者： Deng-Yin Jiang Li-Sheng Hu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781509046584

This paper considers the contaminant emission diffusion monitoring problem by applying the tensor approach for the industrial-environmental *** emission contaminant concentration is used from an embedded sensor for computation of features and monitoring of contaminant diffusion.A reduced feature subset,which is optimal in both estimation and clustering least squares errors,is then selected using a new dominant feature monitoring algorithm to reduce the signal processing and number of sensors *** matrix based subspace method can't capture the spatiotemporal characteristics *** tensor space method is proposed to be used to monitor the contaminant concentration in environmental protection *** order to fit the multiple invariance of the measurement output tensor data processing for contaminant emission diffusion monitoring,the non-negative tensor factorization model is proposed to analyze the tensor data,which stems from uniqueness of low-rank decomposition of higher-order *** using the non-negative tensor factorization,the estimated latent contaminant concentration data structure combing with the covariance-based algorithm are given to derive the metric of contaminant concentration in the environmental protection *** concentration is then measured using non-negative tensor factorization with observable data based on the reduced features.A simulation example is provided to test the effectiveness and advantages of proposed method using tensor method with only the dominant features measurement.

关键词： Contaminants dynamic emission Contaminant diffusion process Dominant feature analysis non-negative tensor factorization Contaminants monitoring

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Application of non-negative tensor factorization in Intelligent Fault Diagnosis of Gearboxes

Application of Non-Negative Tensor Factorization in Intellig...

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2nd International Conference on Manufacturing Science and Engineering

作者： Peng, Sen Xu, Feiyun Ji, Minping Hu, Jianzhong Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China

ISBN: (纸本)9783037850398

In this paper, a novel approach is proposed to extract features from the bispectra of mechanical fault signals using non-negative tensor factorization (NTF) for the purpose of establishing an intrinsic relationship between mechanical faults and extracted characteristics. First, the bispectra of mechanical signals are obtained and stacked to form a three-dimensional (3D) tensor. Then, an NTF method is applied to extract features, which are represented by a series of "basis images," from this tensor. Finally, coefficients indicating these basis images' weights in constituting original bispectral images are calculated for fault classification. Experiments on fault datasets of gearboxes showed that the results of the proposed method not only reveal some nonlinear features of the system, but also have low data dimensions and intuitive meanings with regard to fault characteristic frequencies, which bring great convenience to an explanation of the relationship between mechanical faults and the corresponding bispectra.

关键词： non-negative tensor factorization Feature Extraction Higher-Order Spectrum Gearbox

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Outlier Detection on Power Consumption via non-negative tensor factorization Models 25

Outlier Detection on Power Consumption via Non-negative Tens...

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Kurutmaz, M. Burak Cemgil, A. Taylan Cezayirli, Ahmet Bingol, Ezgi Sener, Sait Bogazici Univ Bilgisayar Muhendisligi Bolumu TR-34342 Istanbul Turkey Netas Telekomunikasyon AS TR-34912 Istanbul Turkey

ISBN: (纸本)9781509064946

Missing data and outliers are encountered frequently in signal processing problems. Presence of outliers can reduce the performance in completion of missing values or interpolation. In this work, we present a method based on non-negative tensor Factoriazation models that is able to solve interpolation and finding consuming characteristics problems on electrical power consumption data with high performance.

关键词： non-negative tensor factorization Missing Data Outlier Interpolation Power Consumption

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ENDMEMBER CONSTRAINT non-negative tensor factorization VIA TOTAL VARIATION FOR HYPERSPECTRAL UNMIXING

ENDMEMBER CONSTRAINT NON-NEGATIVE TENSOR FACTORIZATION VIA T...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Jin-Ju Wang, Ding-Cheng Huang, Ting-Zhu Huang, Jie Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781665403696

Hyperspectral unmixing (HU), estimating endmembers and the corresponding abundances, is crucial for the development of hyperspectral images (HSIs). To improve the unmixing performance, various spatial regularizers are imposed on the abundance matrix. Note that endmember information is also important for HU, especially when the spectral signature in HSIs are highly correlated. In this paper, we investigate information from both endmembers and abundances and propose an endmember constraint non-negative tensor factorization via total variation (EC-NTF-TV) for HU. For estimating endmembers, we introduce an endmember constraint to alleviate the spectral signatures' high correlation. In addition, we adopt the TV regularization to exploit the spatial correlation in abundance maps. Finally, we solve the proposed model under the augmented multiplicative update framework. Both synthetic and real hyperspectral data experiments demonstrate the effectiveness of the proposed algorithm.

关键词： Hyperspectral unmixing endmember constraint non-negative tensor factorization

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Fast non-negative tensor factorization based on CP decomposition for 3D Facial Expression Recognition 14

Fast Non-Negative Tensor Factorization based on CP decomposi...

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14th IEEE International Conference on Signal Processing (ICSP)

作者： Jiang, Yajie Ruan, Qiuqi An, Gaoyun Jin, Yi Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China

ISBN: (纸本)9781538646731

tensor is a representation of multi-dimensional array and it has widely and universal application of processing high dimensional data. According to the non-negativity of tensor data, the algorithm fast non-negative tensor factorization (FNNTF) has been investigated to explicitly model 3D facial expressions. Using a 4th-order tensor to describe original facial data and utilizing CANDECOMP/ PARAFAC (CP) decomposition which express data as factor matrix form to establish the tensor model. The framework of alternating non-negative least squares (ANLS) is used to depict the objective function of FFNTF which is a low-rank tensor approximate problem based on CP decomposition with nonnegative constrained condition (NNCP). To simplify problems, the objective function of FNNTF is dissolved into a group of subproblems in which the index set has been separated into two subsets. The index subsets are used for variables exchange when the solutions of subproblems are negative. This procedure effectively reduces computation and time cost of tensor-based method and enhances the average recognition rate for 3D facial expression recognition. FNNTF performs on BU-3DFE database achieves an average recognition rate of 86.1% for the six facial expressions. Especially, for the case of surprise (SU), our average recognition rate reaches to 98.9%. In summary, the proposed method achieves state-of-theart or comparable performance.

关键词： tensor CP decomposition non-negative tensor factorization subproblem 3D Facial Expression Recognition

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