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6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Oka, Kaito Igarashi, Masaki Shimada, Atsushi Taniguchi, Rin-ichiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan Kyushu Univ Fac Arts & Sci Fukuoka Japan

ISBN: (纸本)9789897582226

Although people flow analysis is widely studied because of its importance, there are some difficulties with previous methods, such as the cost of sensors, person re-identification, and the spread of smartphone applications for collecting data. Today, Probe Request sensing for people flow analysis is gathering attention because it conquers many of the difficulties of previous methods. We propose a framework for Probe Request data analysis for extracting the latent behavior patterns of people. To make the extracted patterns understandable, we apply a non-negative tensor factorization with a sparsity constraint and initialization with prior knowledge to the analysis. Experimental result showed that our framework helps the interpretation of Probe Request data.

关键词： Probe Request People Flow Location Information non-negative tensor factorization Data Mining

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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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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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Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using non-negative tensor factorization

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INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2016年第1期14卷 36-49页

作者： Han, Yufei Moutarde, Fabien INRIA RITS Domaine Voluceau F-78150 Rocquencourt France PSL Res Univ Mines ParisTech Robot Lab CAOR 60 Blvd St Michel F-75006 Paris France

In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized non-negative tensor factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.

关键词： Large-scale traffic dynamics non-negative tensor factorization

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

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

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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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Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization

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NEUROCOMPUTING 2015年 170卷 318-327页

作者： Figueiredo, Marisa Ribeiro, Bernardete de Almeida, Ana Univ Coimbra Dept Informat Engn CISUC Polo 2 P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn CISUC Polo 2 P-3030290 Coimbra Portugal Inst Univ Lisboa ISCTE Univ Ctr Lisbon P-1649026 Lisbon Portugal

This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle. (C) 2015 Elsevier B.V. All rights reserved.

关键词： non-negative tensor factorization Electrical signal disaggregation non-intrusive load monitoring (NILM) Energy efficiency

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Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2015年第5期22卷 1009-1019页

作者： Luo, Yuan Xin, Yu Hochberg, Ephraim Joshi, Rohit Uzuner, Ozlem Szolovits, Peter MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Harvard Univ Sch Med Massachusetts Gen Hosp Ctr Lymphoma Cambridge MA 02138 USA Harvard Univ Sch Med Dept Med Cambridge MA 02138 USA SUNY Albany Dept Informat Studies Albany NY 12222 USA

Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.

关键词： non-negative tensor factorization unsupervised learning subgraph mining natural language processing

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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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Audio source separation using a redundant library of source spectral bases for non-negative tensor factorization 40

Audio source separation using a redundant library of source ...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Fakhry, Mahmoud Svaizer, Piergiorgio Omologo, Maurizio University of Trento via Sommarive 5 Trento Italy Center of Information Technology Fondazione Bruno Kessler - Irst via Sommarive 18 Trento Italy

ISBN: (纸本)9781467369978

This work proposes a solution to the problem of under-determined audio source separation using pre-trained redundant source-based prior information. In local Gaussian modeling of a mixing process, an observed mixture is modeled by a Gaussian distribution parameterized by source variances and spatial covariance matrices. The separation is performed by estimating the parameters, and applying Wiener filtering on the observed mixture. We propose, in a training phase, to build a redundant library of spectral basis matrices of all probable source power spectra, applying non-negative tensor factorization (NTF). In the testing phase, the matrices that match the observed mixture are detected using NTF. With the help of the detected matrices, a maximum likelihood algorithm is proposed in order to iteratively estimate the parameters of the model, exploiting the spatial redundancy of the observed mixture and using NTF. The proposed algorithm proves more flexibility and efficiency with respect to a baseline algorithm used as a reference. © 2015 IEEE.

关键词： audio source separation model parameters non-negative tensor factorization redundant library Spectral bases

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