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A high performance hardware architecture for non-negative tensor factorization

MICROELECTRONICS JOURNAL 2019年第Mar.期85卷 25-33页

作者： Min, Biao Huang, Wei-pei Cheung, Ray C. C. Yan, Hong City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

non-negative tensor factorization (NTF) algorithm is an emerging method for high-dimensional data analysis, which is applied in many fields such as computer vision, and bioinformatics. This paper presents an effective method to accelerate NTF computations and proposes a corresponding hardware architecture, which consists of multiple processing units. The decomposed factors are calculated by using shared intermediate results. By using the proposed method, NTF can be implemented in parallel and hardware resources can be saved by sharing. In this paper, we evaluate the proposed architecture on Xilinx Virtex-6 FPGA XC6VLX760T and apply it into 2 applications, i.e. video background estimation and facial images processing. The experimental results show that the proposed hardware architecture achieves over 80 times faster than CPU implementation of NTF. Compared with the implementations on GPGPU, the proposed architecture achieves nearly the same speedup. While the strategy in this paper is applied to GPU platforms, the execution time can be reduced by a half, due to the computational sharing in this paper.

关键词： non-negative tensor factorization High-dimensional data Hardware architecture Field-programmable gate array (FPGA) Parallel architecture

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Hyperspectral image classification using non-negative tensor factorization and multinomial logistic regression

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JOURNAL OF APPLIED REMOTE SENSING 2019年第2期13卷

作者： Mirzaei, Sayeh Univ Tehran Coll Engn Sch Engn Sci Tehran Iran

We aim to classify every pixel of a hyperspectral image. Toward this goal, we first decompose the image data. The three-dimensional (3-D) tensor of an image cube is decomposed to the spectral signatures and abundance matrix using non-negative tensor factorization (NTF) methods. In contrast to the matrix factorization where the pixels' spectra are stacked in columns of the data matrix, the NTF techniques preserve the spatial structure of the image data. Therefore, the obtained abundance maps provide discriminant spatial features for classification. Morphological attribute profiles are also computed for abundance maps and their effect on the classification performance is studied. Using the original spectral image and the obtained spatial features of the abundance matrices, we construct a composite kernel framework. We apply a multinomial logistic regression classifier to the kernels. Experiments show that, in terms of the classification accuracy, the proposed feature sets acquired through NTF techniques can lead to a better performance compared to the principal component analysis and non-negative matrix factorization feature sets. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral image classification non-negative tensor factorization multinomial logistic regression

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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

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JOURNAL OF COMPUTATIONAL PHYSICS 2019年 395卷 85-104页

作者： Vesselinov, V. V. Mudunuru, M. K. Karra, S. O'Malley, D. Alexandrov, B. S. Los Alamos Natl Lab Earth & Environm Sci Div Computat Earth Sci Grp Los Alamos NM 87545 USA Univ Maryland Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Los Alamos Natl Lab Theoret Div Phys & Chem Mat Grp Los Alamos NM 87545 USA

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using non-negative tensor factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A + B -> C, where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outpu

关键词： non-negative tensor factorization Unsupervised machine learning Structure-preserving feature extraction Reactive-mixing Anisotropic dispersion non-negative solutions

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Application of non-negative tensor factorization for Airport Flight Delay Pattern Recognition

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IEEE ACCESS 2019年 7卷 171724-171737页

作者： Zhong, Han Qi, Geqi Guan, Wei Hua, Xiaochen Beijing Jiaotong Univ MOT Key Lab Transport Ind Big Data Applicat Techn Beijing 100044 Peoples R China Civil Aviat Univ China Coll Air Traff Management Tianjin 300300 Peoples R China Tianjin Subbur Air Traff Management Tianjin 300000 Peoples R China

With the rapid development of civil aviation transportation in China, huge demand growth has broken the balance between supply and demand, resulting in airspace congestion and increasing flight delays. The delays of large airports have been increasing year by year, which has seriously affected the air travel experience of passengers. Obtaining their flight delay patterns can help identify defects in flight scheduling and airspace utilization. The investigation based on the actual flight operation data of Tianjin Binhai International Airport (TSN) is conducted, in order to capture the relationship and impact between the factors such as traffic flow direction, airline attributes and hourly average delay distribution. Furthermore, non-negative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and Block Coordinate Descent (BCD) algorithm for selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of the Tianjin airport delay are extracted, which can provide some new perspectives for air traffic management unit to alleviate airspace congestion and improve service quality.

关键词： Air traffic flight delay non-negative tensor factorization pattern recognition

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Stereophonic Music Separation Based on non-negative tensor factorization with Cepstral Distance Regularization

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2018年第7期E101A卷 1057-1064页

作者： Seki, Shogo Toda, Tomoki Takeda, Kazuya Nagoya Univ Grad Sch Informat Nagoya Aichi 4648603 Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi 4648603 Japan Nagoya Univ Inst Innovat Future Soc Nagoya Aichi 4648603 Japan

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. non-negative tensor factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

关键词： stereophonic music separation non-negative tensor factorization cepstral distance regularization

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Binaural Localization of Multiple Sound Sources by non-negative tensor factorization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第6期26卷 1068-1078页

作者： Benaroya, Elie Laurent Obin, Nicolas Liuni, Marco Roebel, Axel Raumel, Wilson Argentieri, Sylvain Sorbonne Univ CNRS IRCAM F-75005 Paris France Sorbonne Univ CNRS IRCAM STMS F-75005 Paris France Sorbonne Univ CNRS ISIR F-75005 Paris France

This paper presents non-negative factorization of audio signals for the binaural localization of multiple sound sources within realistic and unknown sound environments. non-negative tensor factorization (NTF) provides a sparse representation of multichannel audio signals in time, frequency, and space that can be exploited in computational audio scene analysis and robot audition for the separation and localization of sound sources. In the proposed formulation, each sound source is represented by means of spectral dictionaries, temporal activation, and its distribution within each channel (here, left and right ears). This distribution, being dependent on the frequency, can be interpreted as an explicit estimation of the Head-Related Transfer Function (HRTF) of a binaural head which can then be converted into the estimated sound source position. Moreover, the semisupervised formulation of the non-negative factorization allows us to integrate prior knowledge about some sound sources of interest whose dictionaries can be learned in advance, whereas the remaining sources are considered as background sound, which remains unknown and is estimated on the fly. The proposed NTF-based sound source localization is applied here to binaural sound source localization of multiple speakers within realistic sound environments.

关键词： Binaural localization robot audition computational audio scene analysis non-negative tensor factorization

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non-negative tensor factorization for Human Behavioral Pattern Mining in Online Games

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INFORMATION 2018年第3期9卷 66页

作者： Sapienza, Anna Bessi, Alessandro Ferrara, Emilio USC Inst Informat Sci Marina Del Rey CA 90292 USA

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the non-negative tensor factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation.

关键词： non-negative tensor factorization temporal and topological pattern mining human behavior multiplayer online game

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VARIATIONAL BAYESIAN PARTIALLY OBSERVED non-negative tensor factorization 28

VARIATIONAL BAYESIAN PARTIALLY OBSERVED NON-NEGATIVE TENSOR ...

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IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Hinrich, Jesper L. Nielsen, Soren F. V. Madsen, Kristoffer H. Morup, Morten Tech Univ Denmark DTU Compute Lyngby Denmark Univ Copenhagen Hosp Hvidovre Danish Res Ctr Magnet Resonance Copenhagen Denmark

ISBN: (纸本)9781538654774

non-negative matrix and tensor factorization (NMF/NTF) have become important tools for extracting part based representations in data. It is however unclear when an NMF or NTF approach is most suited for data and how reliably the models predict when trained on partially observed data. We presently extend a recently proposed variational Bayesian NMF (VB-NMF) to non-negative tensor factorization (VB-NTF) for partially observed data. This admits bi-and multilinear structure quantification considering both model prediction and evidence. We evaluate the developed VB-NTF on synthetic and a real dataset of gene expression in the human brain and contrast the performance to VB-NMF and conventional NMF/NTF. We find that the gene expressions are better accounted for by VB-NMF than VB-NTF and that VB-NMF/VB-NTF more robustly handle partially observed data than conventional NMF/NTF. In particular, probabilistic modeling is beneficial when large amounts of data is missing and/or the model order over-specified.

关键词： probabilistic modeling missing data human brain microarray data 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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Identifying regional service function from PM_2.5 mass concentration throughout a city with non-negative tensor factorization approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2017年第35期24卷 26893-26900页

作者： Hu, Dongmei Zhou, Yang Xu, Ming Wu, Jianping Du, Yiman Song, Bingyue Hu, Kezhen Tsinghua Univ Coll Civil Engn Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

This paper examines the holistic viewpoint on pollution pattern from time, day, and region dimensions based on the public data of fine particle concentrations, which cover 35 ambient monitoring stations in Beijing firstly. According to data driven, non-negative tensor factorization (NTF) method is introduced to distinguish pollution patterns which could identify the area service function. Results show that five patterns are obtained and annotated as traffic, industrial, residential, commercial, and steady ones. Each type owns special characteristics on time basis or day basis. Furthermore, calculating the reconstruction correlation of tensors with respect to sites, time, and days approximately approaches to 0.95-0.96, and it can be employed with high evaluation values of the model. Comparing with the original classifications drew by land use, this method corresponds with the reality well for considering the changes of surrounding sources. Some commendations on public travel and controlling measures based on local pollution presented in this study can be provided for further decrease of fine particle and improvement of air quality.

关键词： Pollution pattern PM2.5 Regional service function non-negative tensor factorization

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