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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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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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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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Electrical Signal Source Separation Via nonnegative tensor factorization Using On Site Measurements in a Smart Home

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2014年第2期63卷 364-373页

作者： Figueiredo, Marisa Ribeiro, Bernardete de Almeida, Ana Univ Coimbra Ctr Informat & Syst P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Univ Inst Lisbon ISCTE IUL P-1600042 Lisbon Portugal Univ Coimbra Ctr Informat & Syst P-3030290 Coimbra Portugal

Measuring the electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's electrical signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.

关键词： Electrical signal disaggregation nonintrusive load monitoring nonnegative tensor factorization single-channel source separation sparse coding

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A proximal ANLS algorithm for nonnegative tensor factorization with a periodic enhanced line search

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APPLICATIONS OF MATHEMATICS 2013年第5期58卷 493-509页

作者： Bunker, Douglas Han, Lixing Zhang, Shuhua Univ Michigan Dept Math Flint MI 48502 USA Tianjin Univ Finance & Econ Tianjin 300222 Peoples R China

The Alternating nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method. We present a Proximal ANLS (PANLS) algorithm to enforce convergence. To speed up the PANLS method, we propose to combine it with a periodic enhanced line search strategy. The resulting algorithm, PANLS/PELS, converges to a critical point of the nonnegative tensor factorization problem under mild conditions. We also provide some numerical results comparing the ANLS and PANLS/PELS methods.

关键词： nonnegative tensor factorization proximal method alternating least squares enhanced line search global convergence

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Tag Refinement for User-Contributed Images via Graph Learning and nonnegative tensor factorization

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IEEE SIGNAL PROCESSING LETTERS 2015年第9期22卷 1302-1305页

作者： Qian, Zhiming Zhong, Ping Wang, Runsheng Natl Univ Def Technol Sch Elect Sci & Engn Natl Key Lab Automat Target Recognit ATR Changsha 410073 Hunan Peoples R China

Social image tagging systems mostly suffer from poor performance for image retrieval due to the noisy and incomplete correspondences between user-contributed images and their associated tags. In this letter, we aim to refine tag allocations in the social tagging data provided by these systems. In particular, we propose to harness the tagged and untagged data with a two-stage strategy according to different types of data relations, i.e. item similarity defined by prior knowledge and item co-occurrence learned from data statistics. To solve the sparsity problem, we first introduce a new graph learning (GL) method for enriching the tagging data according to item similarities. Then, we develop a method of nonnegative tensor factorization (NTF) for learning more coherent ternary relations among users, images and tags coupled by the manifold constraints learned from item co-occurrences. Experimental results with the tagging data from the NUS-WIDE dataset have been reported to validate the effectiveness of the proposed method.

关键词： Graph learning image retrieval nonnegative tensor factorization tag refinement

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Constrained nonnegative tensor factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第4期16卷 583-587页

作者： Feng, Boyu Wang, Jinfei Univ Western Ontario Dept Geog London ON N6A 5C2 Canada

In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time.

关键词： Constraints hyperspectral imagery nonnegative tensor factorization spectral unmixing

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Coding-Based Informed Source Separation: nonnegative tensor factorization Approach

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2013年第8期21卷 1699-1712页

作者： Ozerov, Alexey Liutkus, Antoine Badeau, Roland Richard, Gael Technicolor Res & Innovat F-35576 Cesson Sevigne France Telecom ParisTech CNRS LTCI Inst Mines Telecom F-75014 Paris France

Informed source separation (ISS) aims at reliably recovering sources from a mixture. To this purpose, it relies on the assumption that the original sources are available during an encoding stage. Given both sources and mixture, a side-information may be computed and transmitted along with the mixture, whereas the original sources are not available any longer. During a decoding stage, both mixture and side-information are processed to recover the sources. ISS is motivated by a number of specific applications including active listening and remixing of music, karaoke, audio gaming, etc. Most ISS techniques proposed so far rely on a source separation strategy and cannot achieve better results than oracle estimators. In this study, we introduce Coding-based ISS (CISS) and draw the connection between ISS and source coding. CISS amounts to encode the sources using not only a model as in source coding but also the observation of the mixture. This strategy has several advantages over conventional ISS methods. First, it can reach any quality, provided sufficient bandwidth is available as in source coding. Second, it makes use of the mixture in order to reduce the bitrate required to transmit the sources, as in classical ISS. Furthermore, we introduce nonnegative tensor factorization as a very efficient model for CISS and report rate-distortion results that strongly outperform the state of the art.

关键词： Informed source separation spatial audio object coding source coding constrained entropy quantization probabilistic model nonnegative tensor factorization

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A Generalized nonnegative tensor factorization Approach for Distant Speech Recognition With Distributed Microphones

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2016年第10期24卷 1721-1731页

作者： Mirsamadi, Seyedmahdad Hansen, John H. L. Univ Texas Dallas Ctr Robust Speech Syst Richardson TX 75080 USA

Automatic speech recognition (ASR) using distant (far-field) microphones is a challenging task, in which room reverberation is one of the primary causes of performance degradation. This study proposes a multichannel spectral enhancement method 14 reverberation-robust ASR using distributed microphones. The proposed method uses the techniques of nonnegative tensor factorization in order to identify the clean speech component from a set of observed reverberant spectrograms from the different channels. The general family of alpha-beta divergences is used for the tensor decomposition task which provides increased flexibility for the algorithm and is shown to provide improvements in highly reverberant scenarios. Unlike many conventional array processing solutions, the proposed method does not require closely-spaced microphones and is independent of source and microphone locations. The algorithm can automatically adapt to unbalanced direct-to reverberation ratios among different channels, which is useful in blind scenarios in which no information is available about source to -microphone distances. For a medium vocabulary distant ASR task based on TIMIT utterances, and using clean-trained deep neural network acoustic models, absolute WER improvements of +17.2%, +20.7%, and +23.2% are achieved in single-channel, two channel, and four-channel scenarios.

关键词： Distant speech recognition distributed far-field microphones nonnegative tensor factorization reverberation

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Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation

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NEUROCOMPUTING 2016年 198卷 148-154页

作者： Zhang, Yu Zhou, Guoxu Zhao, Qibin Cichocki, Andrzej Wang, Xingyu E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China RIKEN Brain Sci Inst Lab Adv Brain Signal Proc 2-1 Hirosawa Wako Saitama 3510198 Japan Skolkowo Inst Sci & Technol Moscow Russia

nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, most of the existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. In this study, we propose a new algorithm called FastNTF_APG to speed up NTF by combining accelerated proximal gradient and low-rank approximation. Experimental results demonstrate that FastNTF_APG achieves significantly higher computational efficiency than state-of-the-art NTF algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： CP (PARAFAC) decompositions nonnegative tensor factorization Accelerated proximal gradient Low-rank approximation

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