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non-negative tensor factorization-based dependence map analysis for local damage detection in presence of non-Gaussian noise

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MEASUREMENT 2025年 248卷

作者： Michalak, Anna Hebda-Sobkowicz, Justyna Kumar, Anil Zimroz, Radoslaw Zdunek, Rafal Wylomanska, Agnieszka Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Na Grobli 15 PL-50421 Wroclaw Poland Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Wroclaw Univ Sci & Technol Fac Elect Photon & Microsyst Wroclaw Poland Wroclaw Univ Sci & Technol Fac Pure & Appl Math Hugo Steinhaus Ctr Hoene Wronskiego 13 C PL-50376 Wroclaw Poland

The time-frequency map (TFM) is frequently used in condition monitoring, necessitating further processing to select an informative frequency band (IFB) or directly detect damage. However, selecting an IFB is challenging due to the complexity of spectral structures, non-Gaussian disturbances, and overlapping fault signatures in vibration signals. Additionally, dynamic operating conditions and low signal-to-noise ratio further complicate the identification of relevant features that indicate damage. To solve this problem, the present work proposes a novel method for informative band selection and local damage detection in rolling element bearings, utilizing non-negative tensor factorization (NTF)-based dependence map analysis. The recently introduced concept of the dependence map is leveraged, with a set of these maps being factorized to separate informative components from non-informative ones. Dependence maps provide valuable information on the auto-similarity of spectral content, while NTF, a powerful tool commonly used in image processing for feature extraction, enhances this process. The combination of these methods allows for the extraction of IFBs, forming the basis for local damage detection. The effectiveness of the proposed method has been validated using both synthetic and real vibration signals corrupted with non-Gaussian disturbances.

关键词： Fault detection Bearings Vibration Measures of dependence non-negative tensor factorization

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On Ambisonic Source Separation With Spatially Informed non-negative tensor factorization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 3238-3255页

作者： Guzik, Mateusz Kowalczyk, Konrad AGH Univ Krakow Inst Elect Fac Comp Sci Elect & Telecommun Signal Proc Grp PL-30059 Krakow Poland

This article presents a non-negative tensor factorization based method for sound source separation from Ambisonic microphone signals. The proposed method enables the use of prior knowledge about the Directions-of-Arrival (DOAs) of the sources, incorporated through a constraint on the Spatial Covariance Matrix (SCM) within a Maximum a Posteriori (MAP) framework. Specifically, this article presents a detailed derivation of four algorithms that are based on two types of cost functions, namely the squared Euclidean distance and the Itakura-Saito divergence, which are then combined with two prior probability distributions on the SCM, that is the Wishart and the Inverse Wishart. The experimental evaluation of the baseline Maximum Likelihood (ML) and the proposed MAP methods is primarily based on first-order Ambisonic recordings, using four different source signal datasets, three with musical pieces and one containing speech utterances. We consider under-determined, determined, as well as over-determined scenarios by separating two, four and six sound sources, respectively. Furthermore, we evaluate the proposed algorithms for different spherical harmonic orders and at different reverberation time levels, as well as in non-ideal prior knowledge conditions, for increasingly more corrupted DOAs. Overall, in comparison with beamforming and a state-of-the-art separation technique, as well as the baseline ML methods, the proposed MAP approach offers superior separation performance in a variety of scenarios, as shown by the analysis of the experimental evaluation results, in terms of the standard objective separation measures, such as the SDR, ISR, SIR and SAR.

关键词： Source separation non-negative tensor factorization ambisonics spherical harmonics localization prior

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non-negative tensor factorization for vibration-based local damage detection

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期198卷

作者： Gabor, Mateusz Zdunek, Rafal Zimroz, Radoslaw Wodecki, Jacek Wylomanska, Agnieszka Wroclaw Univ Sci & Technol Fac Elect Photon & Microsyst Janiszewskiego 11 PL-50372 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Grobli 15 PL-50421 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Pure & Appl Math Hugo Steinhaus Ctr Hoene Wronskiego 13c PL-50376 Wroclaw Poland

In this study, a novel non-negative tensor factorization (NTF)-based method for vibration -based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-frequency method is frequently used as a primary decomposition technique. It is proposed here to extract multi-linear NTF-based components from a 3D array of time-frequency representations of an observed signal partitioned into blocks. As a result, frequency and temporal informative components can be efficiently separated from non-informative ones. The experiments performed on synthetic and real signals demonstrate the high efficiency of the proposed method with respect to the already known non-negative matrix factorization approach.

关键词： Fault detection Bearings Vibration non-negative matrix factorization non-negative tensor factorization

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General-purpose Unsupervised Cyber Anomaly Detection via non-negative tensor factorization

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DIGITAL THREATS: RESEARCH AND PRACTICE 2023年第1期4卷 1-28页

作者： Eren, Maksim E. Moore, Juston S. Skau, Erik Moore, Elisabeth Bhattarai, Manish Chennupati, Gopinath Alexandrov, Boian S. Los Alamos Natl Lab Adv Res Cyber Syst Los Alamos NM 87545 USA Los Alamos Natl Lab Informat Sci Los Alamos NM USA Los Alamos Natl Lab Div Theoret Los Alamos NM USA Alexa Amazon Bellevue WA USA

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. non-negative tensor factorization, however, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

关键词： Anomaly detection Poisson tensor factorization non-negative tensor factorization unsupervised learning cyber security CPD malware data fusion ensemble learning GPU

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Robust Video Hashing with non-negative tensor factorization for Copy Detection 24

Robust Video Hashing with Non-negative Tensor Factorization ...

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4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Yu, Mengzhu Tang, Zhenjun Zhuang, Huijiang Liang, Xiaoping Li, Zhixin Zhang, Xianquan Guangxi Normal Univ Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin Peoples R China Guangxi Normal Univ Guangxi Key Lab MultiSource Informat Min & Secur Guilin Peoples R China

ISBN: (纸本)9798400706028

Copy detection is a key task of video copyright protection. This paper presents a robust video hashing with non-negative tensor factorization (NTF) for copy detection. In the presented video hashing scheme, secondary frames are computed from the preprocessed video by assigning weights to all frames within a video group based on color entropy. Next, the secondary frames are fed into the pre-trained MobileNetV2 and then NTF is exploited to compress the three-order tensor constructed by stacking the output feature maps for hash construction. Experiments conducted on publicly available video datasets indicate that the presented hashing scheme outperforms the evaluated hashing schemes in the performances of classification and copy detection.

关键词： Video Hashing Color Entropy non-negative tensor factorization MobileNetV2 Copy Detection

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Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

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JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2023年第2期6卷 1179-1192页

作者： Matsui, Akira Kobayashi, Teruyoshi Moriwaki, Daisuke Ferrara, Emilio Univ Southern Calif Dept Comp Sci Los Angeles CA USA Kobe Univ Dept Econ Kobe Japan Kobe Univ Ctr Computat Social Sci Kobe Japan CyberAgent Inc AI Lab Shibuya Tokyo Japan Univ Southern Calif Dept Commun Los Angeles CA 90007 USA Univ Southern Calif Informat Sci Inst Los Angeles CA 90007 USA

Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intra- and inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.

关键词： Multi-timescale patterns Consumer behavior Consumption expenditure non-negative tensor factorization

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Affective Color Palette Recommendations with non-negative tensor factorization 26

Affective Color Palette Recommendations with Non-negative Te...

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26th International Conference Information Visualisation (IV)

作者： Morita, Ikuya Takahashi, Shigeo Nishimura, Satoshi Misue, Kazuo Univ Aizu Aizu Wakamatsu Japan Univ Tsukuba Tsukuba Japan

ISBN: (纸本)9781665490078

Color is an essential factor that influences human perception, and thus, the proper selection of color sets is crucial in creating informative and appealing visual content. Furthermore, the choice of such color palettes often reflects the underlying emotional intention of creators, especially when they want to introduce specific affective styles. This paper presents a color palette recommendation system that facilitates preferred colors and affective expressions in visual content. This is accomplished by introducing non-negative tensor factorization (NTF), which extends the conventional matrix-based collaborative filtering for recommending items through ratings of multiple users. In our approach, we composed a rating tensor that constitutes the scores for colors in terms of affective factors provided by participants in the user study. With this rating tensor, we explored the meaningful relation between affective expression and color preference. Our experiments exposed that we can successfully apply a tensor-based approach to recommending convincing sets of colors in several possible cases by predicting the underlying emotional intentions in the visual content design.

关键词： Color palettes affective factors recommendation systems non-negative tensor factorization

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WISHART LOCALIZATION PRIOR ON SPATIAL COVARIANCE MATRIX IN AMBISONIC SOURCE SEPARATION USING non-negative tensor factorization 47

WISHART LOCALIZATION PRIOR ON SPATIAL COVARIANCE MATRIX IN A...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Guzik, Mateusz Kowalczyk, Konrad AGH Univ Sci & Technol Inst Elect Krakow Poland

ISBN: (纸本)9781665405409

This paper presents an extension of the existing non-negative tensor factorization (NTF) based method for sound source separation under reverberant conditions, formulated for Ambisonic microphone mixture signals. In particular, we address the problem of optimal exploitation of the prior knowledge concerning the source localization, through the formulation of a suitable Maximum a Posteriori (MAP) framework. Within the presented approach, the magnitude spectrograms are modelled by the NTF and the individual source Spatial Covariance Matrices (SCM) are approximated as a sum of anechoic Spherical Harmonic (SH) components, weighted with the so-called spatial selector. We constrain the SCM using the Wishart distribution, which leads to a new posterior probability and in turn to the derivation of the extended update rules. The proposed solution avoids the issues encountered in the original method, related to the empirical binary initialization strategy for the spatial selector weights, which due to multiplicative update rules may result in sound coming from certain directions not being taken into account. The proposed method is evaluated against the original algorithm and another recently proposed Expectation Maximization (EM) algorithm that also incorporates a spatial localization prior, showing improved separation performance in experiments with first-order Ambisonic recordings of musical instruments and speech utterances.

关键词： source separation non-negative tensor factorization ambisonics spherical harmonics array signal processing

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non-negative Einstein tensor factorization for unmixing hyperspectral images

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NUMERICAL ALGORITHMS 2025年 1-31页

作者： El Hachimi, Anas Jbilou, Khalide Ratnani, Ahmed Mohammed VI Polytech Univ Vanguard Ctr UM6P Lot 660 Ben Guerir 43150 Morocco Univ Littoral Cote dOpale LMPA 50 Rue F Buisson F-62228 Calais France

In this manuscript, we introduce a tensor-based approach to non-negative tensor factorization (NTF). The method entails tensor dimension reduction through the utilization of the Einstein product. To maintain the regularity and sparsity of the data, certain constraints are imposed. Additionally, we present an optimization algorithm in the form of a tensor multiplicative updates method, which relies on the Einstein product. To guarantee a minimum number of iterations for the convergence of the proposed algorithm, we employ the Reduced Rank Extrapolation (RRE) and the Topological Extrapolation Transformation Algorithm (TEA). The efficacy of the proposed model is demonstrated through tests conducted on Hyperspectral Images (HI) for denoising, as well as for Hyperspectral Image Linear Unmixing. Numerical experiments are provided to substantiate the effectiveness of the proposed model for both synthetic and real data.

关键词： tensor non-negative tensor factorization Hyperspectral images The Einstein tensor product The reduced rank extrapolation The topological extrapolation transformation algorithm Linear unmixing Denoising

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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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