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nonnegative tensor factorization based on Low-Rank Subspace for Facial Expression Recognition

MOBILE NETWORKS & APPLICATIONS 2022年第1期27卷 58-69页

作者： Liu, Xingang Li, Chenqi Dai, Cheng Lai, Jinfeng Chao, Han-Chieh Univ Elect Sci & Technol China Chengdu Peoples R China Natl Dong Hwa Univ Taiwan Peoples R China

Important progresses have been made in the field of artificial intelligence in recent years, and facial expression recognition (FER), which could greatly facilitate the development of human-computer interaction, has been becoming a significant research hotspot. In this paper, a novel nonnegative tensor factorization method is proposed based on low-rank subspace (NTF-LRS) for FER. Firstly, in order to find the high order correlations underlying multi-dimensional data, a data tensor model is constructed, which could represent different dimensional features ingeniously. And then, the low-rank subspace model is adopted to reconstruct the original tensor model, reduce the redundancy of the learned new tensor, and improve the discriminant abilities of inter-class information. Finally, the reconstructed tensor is decomposed to get factor matrices by nonnegative tensor factorization, where all factor matrices are used to extract subspace features. To verify the effectiveness of our proposal, two well-known facial expression datasets named as "JAFFE" and "CK+" are utilized for evaluation, and the experimental results show that the tensor-based method preserves the original structure of whole samples, which avoids the case of dimension curse because of vectorization. In addition, this method uses Laplacian graph to impose regularization on low-rank subspace model, which keeps the local relationship between sample neighbors.

关键词： Facial expression recognition NTF_LRS tensor representation Subspace model Low-rank nonnegative tensor factorization

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COSEPARABLE nonnegative tensor factorization WITH t-CUR DECOMPOSITION

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SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS 2024年第4期45卷 1805-1826页

作者： Chen, Juefei Huang, Longxiu Wei, Yimin Fudan Univ Sch Math Sci Shanghai 200433 Peoples R China Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48823 USA Michigan State Univ Dept Math E Lansing MI 48823 USA Fudan Univ Key Lab Math Nonlinear Sci Shanghai 200433 Peoples R China

nonnegative matrix factorization (NMF) is an important unsupervised learning method to extract meaningful features from data. To address the NMF problem within a polynomial time framework, researchers have introduced a separability assumption, which has recently evolved into the concept of coseparability. This advancement offers a more efficient core representation for the original data. However, in the real world, the data is more naturally represented as a multidimensional array, such as images or videos. The NMF's application to high-dimensional data involves vectorization, which risks losing essential multidimensional correlations. To retain these inherent correlations in the data, we turn to tensors (multidimensional arrays) and leverage the ten- sort-pro duct. This approach extends the coseparable NMF to the tensor setting, creating what we term coseparable nonnegative tensor factorization (NTF). In this work, we provide an alternating index selection method to select the coseparable core. Furthermore, we validate the t-CUR sampling theory and integrate it with the tensor discrete empirical interpolation method to introduce an alternative, randomized index selection process. These methods have been tested on both synthetic and facial analysis datasets. The results demonstrate the efficiency of coseparable NTF when compared to coseparable NMF.

关键词： nonnegative matrix factorization coseparable nonnegative tensor factorization t-pro duct CUR decomposition

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nonnegative tensor factorization BASED FUSION FOR CHANGES DETECTION IN MULTIRESOLUTION REMOTE SENSING IMAGES

NONNEGATIVE TENSOR FACTORIZATION BASED FUSION FOR CHANGES DE...

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IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)

作者： Benkouider, Yasmine Kheira Benhalouche, Fatima Zohra Karoui, Moussa Sofiane Iftene, Meziane Agence Spatiale Algerienne Ctr Tech Spatiale Arzew Algeria Agence Spatiale Algerienne Arzew Algeria

ISBN: (数字)9781665427951

ISBN: (纸本)9781665427951

Changes detection in multitemporal remote sensing images with different spatial and spectral resolutions remains an important challenge. Most of the changes detection methods that exist in the literature do not take into account this multiresolution issue, and use spatial/spectral resampling process to avoid it. However, this type of procedure can lead to loss of spatial or spectral information. In this paper, a fusion-based approach to detect changes between optical images with different spatial and spectral resolutions is proposed. This approach uses nonnegative tensor decomposition to obtain two unobservable images having the same spatial and spectral resolutions from two observable images acquired by different sensors. Changes detection is done by a pixel-wise difference between these two latent images. Test results on realistic synthetic data demonstrate that the proposed approach have a good potential for changes detection in multiresolution remote sensing images.

关键词： changes detection fusion nonnegative tensor factorization Tucker decomposition nonnegative matrix factorization multiplicative update rules

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Study of performance of low-rank nonnegative tensor factorization methods

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RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING 2023年第4期38卷 231-239页

作者： Shcherbakova, Elena M. Matveev, Sergey A. Smirnov, Alexander P. Tyrtyshnikov, Eugene E. Lomonosov Moscow State Univ Fac Computat Math & Cybernet Moscow 119991 Russia Russian Acad Sci Marchuk Inst Numer Math Moscow 119333 Russia

In the present paper we compare two different iterative approaches to constructing nonnegative tensor train and Tucker decompositions. The first approach is based on idea of alternating projections and randomized sketching for factorization of tensors with nonnegative elements. This approach can be useful for both TT and Tucker formats. The second approach consists of two stages. At the first stage we find the unconstrained tensor train decomposition for the target array. At the second stage we use this initial approximation in order to fix it within moderate number of operations and obtain the factorization with nonnegative factors either in tensor train or Tucker model. We study the performance of these methods for both synthetic data and hyper-spectral image and demonstrate the clear advantage of the latter technique in terms of computational time and wider range of possible applications.

关键词： nonnegative tensor factorization tensor train low rank approximation nonnegative tensor train cross method alternating projections sketching

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Extraction and Prediction of User Communication Behaviors From DNS Query Logs Based on nonnegative tensor factorization

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第3期20卷 2611-2624页

作者： Hatanaka, Kotaro Kimura, Tatsuaki Komai, Yuka Ishibashi, Keisuke Kobayashi, Masahiro Harada, Shigeaki Osaka Univ Dept Informat & Commun Technol Osaka 5650871 Japan NTT DOCOMO Customer Success Dept 1 Tokyo 1006150 Japan NTT Corp NTT Network Technol Labs Tokyo 1818585 Japan Nippon Telegraph & Tel West Corp Business & Mkt Div Osaka 5340024 Japan Int Christian Univ Coll Liberal Arts Div Arts & Sci Tokyo 1818585 Japan NTT Corp NTT Network Serv Syst Labs Tokyo 1818585 Japan

Owing to the critical role of the domain name system (DNS), its query log data are utilized for various network monitoring purposes. With the diversification of network services, these data have become increasingly complex, making mining useful information challenging. DNS query log data can be considered as the superposition of two types of communication patterns: groups of domains accessed simultaneously (e.g., ad servers and content delivery network (CDN) servers) and time-series access patterns based on user behavior characteristics (e.g., access trends during the night). However, previous studies have not focused on extracting both access patterns hidden in the data. This study proposes a method that extracts both patterns of accessed domains and temporal access patterns as user communication behaviors from DNS query log data and predicts future accesses based on these patterns. The proposed method first aggregates similar fully qualified domain names (FQDNs) associated with the same service. We then present temporal regularized nonnegative tensor factorization (TR-NTF) that extracts both access patterns from a third-order tensor expressing DNS query log data and enables prediction. We evaluate the proposed method using synthetic and actual data and demonstrate that it successfully extracts hidden communication patterns and achieves sufficient prediction accuracy.

关键词： DNS user communication behavior nonnegative tensor factorization autoregressive model

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Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2023年第3期15卷 267-280页

作者： Balasubramaniam, Thirunavukarasu Warne, David J. Nayak, Richi Mengersen, Kerrie Queensland Univ Technol Sch Comp Sci 2 George St Brisbane Qld 4000 Australia Queensland Univ Technol Ctr Data Sci 2 George St Brisbane Qld 4000 Australia Queensland Univ Technol Sch Math Sci 2 George St Brisbane Qld 4000 Australia

The world is witnessing the devastating effects of the COVID-19 pandemic. Each country responded to contain the spread of the virus in the early stages through diverse response measures. Interpreting these responses and their patterns globally is essential to inform future responses to COVID-19 variants and future pandemics. A stochastic epidemiological model (SEM) is a well-established mathematical tool that helps to analyse the spread of infectious diseases through communities and the effects of various response measures. However, interpreting the outcome of these models is complex and often requires manual effort. In this paper, we propose a novel method to provide the explainability of an epidemiological model. We represent the output of SEM as a tensor model. We then apply nonnegative tensor factorization (NTF) to identify patterns of global response behaviours of countries and cluster the countries based on these patterns. We interpret the patterns and clusters to understand the global response behaviour of countries in the early stages of the pandemic. Our experimental results demonstrate the advantage of clustering using NTF and provide useful insights into the characteristics of country clusters.

关键词： COVID-19 Stochastic epidemiological modelling Explainable AI nonnegative tensor factorization Clustering Pattern mining

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Localizing hot spots in Poisson radiation data matrices: nonnegative tensor factorization and phase congruency

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JOURNAL OF BIG DATA 2021年第1期8卷 1-16页

作者： Thomason, Michael G. Jordan, Benjamin S. Univ Tennessee Tickle Coll Engn EECS Dept Knoxville TN 37996 USA Univ Tennessee Tickle Coll Engn Nucl Engn Dept Knoxville TN 37996 USA

Detecting and delineating hot spots in data from radiation sensors is required in applications ranging from monitoring large geospatial areas to imaging small objects in close proximity. This paper describes a computational method for localizing potential hot spots in matrices of independent Poisson data where, in numerical terms, a hot spot is a cluster of locally higher sample mean values (higher Poisson intensity) embedded in lower sample mean values (lower background intensity). Two numerical algorithms are computed sequentially for a 3D array of 2D matrices of gross Poisson counts: (1) nonnegative tensor factorization of the 3D array to maximize a Poisson likelihood and (2) phase congruency in pertinent matrices. The indicators of potential hot spots are closed contours in phase congruency in these matrices. The method is illustrated for simulated Poisson radiation datasets, including visualization of the phase congruency contours. The method may be useful in other applications in which there are matrices of nonnegative counts, provided that a Poisson distribution fits the dataset.

关键词： Poisson data Hot spot localization nonnegative tensor factorization Phase congruency

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nonnegative tensor factorization for contaminant source identification

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JOURNAL OF CONTAMINANT HYDROLOGY 2019年第Jan.期220卷 66-97页

作者： Vesselinov, Velimir V. Alexandrov, Boian S. O'Malley, Daniel Los Alamos Natl Lab Earth & Environm Sci Div Computat Earth Sci Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Theoret Div Phys & Chem Mat Grp Los Alamos NM USA

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on nonnegative tensor factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. Thes

关键词： nonnegative tensor factorization Tucker decomposition Feature Extraction Exploratory analysis Blind Source Separation Robustness analysis Unsupervised machine learning Groundwater contamination Source identification Advection-diffusion transport Geochemical signatures

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Relational analysis model of weather conditions and sales patterns based on nonnegative tensor factorization

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第8期58卷 2477-2489页

作者： Okayama, Sei Yamashita, Haruka Mikawa, Kenta Goto, Masayuki Yoshikai, Tomohiro Waseda Univ Grad Sch Creat Sci & Engn Tokyo Japan Sophia Univ Dept Informat & Commun Sci Tokyo Japan Shonan Inst Technol Dept Informat Sci Fujisawa Kanagawa Japan Waseda Univ Sch Creat Sci & Engn Tokyo Japan Japan Weather Assoc Tokyo Japan

It is necessary to analyze the relationships between the retail sales of various items and weather conditions. However, the relationship between the sales of each item and the weather condition may vary among stores. Additionally, it is necessary to model the statistical relationships between a wide variety of goods and weather conditions by using past sales data. In such a case, it becomes unrealistic to construct a forecast model for every individual item owing to the breadth of items and the number of retail shops. This study proposes a model to analyze the relationships between the sales of various items and weather conditions. This method can be used to decompose the data into three matrices based on the nonnegative tensor factorization (NTF) method. The results of the analysis clarified that the proposed model can identify important items whose demand is strongly influenced by weather conditions, thereby increasing the effectiveness of inventory management. Additionally, the store clusters estimated by the proposed model can facilitate the construction of regression models that demonstrate the relationship between the sales of each item and weather conditions.

关键词： weather weather data clustering business analytics nonnegative tensor factorization nonnegative matrix factorization

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Understanding the Spatio-temporal Topic Dynamics of Covid-19 using nonnegative tensor factorization: A Case Study

Understanding the Spatio-temporal Topic Dynamics of Covid-19...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Balasubramaniam, Thirunavukarasu Nayak, Richi Bashar, Md Abul Queensland Univ Technol Ctr Data Sci Sch Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781728125473

Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously. This huge collection of data, if analysed properly, can provide useful insights into people's behavior. More than ever, now is a crucial time under the Covid-19 pandemic to understand people's online behaviors detailing what topics are being discussed, and where (space) and when (time) they are discussed. Given the high complexity and poor quality of the huge social media data, an effective spatio-temporal topic detection method is needed. This paper proposes a tensor-based representation of social media data and Non-negative tensor factorization (NTF) to identify the topics discussed in social media data along with the spatio-temporal topic dynamics. A case study on Covid-19 related tweets from the Australia Twittersphere is presented to identify and visualize spatio-temporal topic dynamics on Covid-19.

关键词： Covid-19 Social media analysis Topic modelling nonnegative tensor factorization Spatio-temporal pattern

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