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Local hierarchical non-negative tensor factorization and its application in machinery fault diagnosis

Journal of Southeast University(English Edition) 2011年第4期27卷 394-399页

作者：王飞许飞云王海军东南大学机械工程学院南京211189

Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.

关键词： non-negative tensor factorization bispectrum feature extraction air compressor BP neural network

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non-negative tensor factorization Applied to Music Genre Classification

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2010年第8期18卷 1955-1967页

作者： Benetos, Emmanouil Kotropoulos, Constantine Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a feature matrix. Consequently, by stacking the feature matrices associated to any dataset recordings, a tensor is created, a fact which necessitates studying music genre classification using tensors. First, a novel algorithm for non-negative tensor factorization (NTF) is derived that extends the non-negative matrix factorization. Several variants of the NTF algorithm emerge by employing different cost functions from the class of Bregman divergences. Second, a novel supervised NTF classifier is proposed, which trains a basis for each class separately and employs basis orthogonalization. A variety of spectral, temporal, perceptual, energy, and pitch descriptors is extracted from 1000 recordings of the GTZAN dataset, which are distributed across 10 genre classes. The NTF classifier performance is compared against that of the multilayer perceptron and the support vector machines by applying a stratified 10-fold cross validation. A genre classification accuracy of 78.9% is reported for the NTF classifier demonstrating the superiority of the aforementioned multilinear classifier over several data matrix-based state-of-the-art classifiers.

关键词： Bregman divergences music genre classification non-negative tensor factorization

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MUSIC GENRE CLASSIFICATION VIA TOPOLOGY PRESERVING non-negative tensor factorization AND SPARSE REPRESENTATIONS

MUSIC GENRE CLASSIFICATION VIA TOPOLOGY PRESERVING NON-NEGAT...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Panagakis, Yannis Kotropoulos, Constantine Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9781424442966

Motivated by the rich, psycho-physiologically grounded properties of auditory cortical representations and the power of sparse representation-based classifiers, we propose a robust music genre classification framework. Its first pilar is a novel multilinear subspace analysis method that reduces the dimensionality of cortical representations of music signals, while preserving the topology of the cortical representations. Its second pilar is the sparse representation based classification, that models any test cortical representation as a sparse weighted sum of dictionary atoms, which stem from training cortical representations of known genre, by assuming that the representations of music recordings of the same genre are close enough in the tensor space they lie. Accordingly, the dimensionality reduction is made in a compatible manner to the working principle of the sparse-representation based classification. Music genre classification accuracy of 93.7% and 94.93% is reported on the GTZAN and the ISMIR2004 Genre datasets, respectively. Both accuracies outperform any accuracy ever reported for state of the art music genre classification algorithms applied to the aforementioned datasets.

关键词： Music genre classification topology preserving non-negative tensor factorization sparse representations

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A Method for Automatic Construction of Ontological Knowledge Bases. III. Automatic Generation of Taxonomy as the Basis for Ontology(*)

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CYBERNETICS AND SYSTEMS ANALYSIS 2016年第3期52卷 365-370页

作者： Marchenko, O. O. Taras Shevchenko Natl Univ Kyiv Kiev Ukraine

A method is developed for the automatic generation of ontological knowledge bases. An algorithm is created for the extraction of explicit semantic relationships between concepts of an ontology on the basis of their semantic-syntactic valence vectors. Vectors of semantic-syntactic valences of words were also used as context vectors in an algorithm for formal concept analysis, which made it possible to develop a method for the automatic generation of high-quality taxonomies. As a result, a basic algorithm for the automatic generation of ontological knowledge bases was developed on the basis of the tensor semantic-syntactic model of natural language.

关键词： automatic knowledge extraction corpus linguistics ontology non-negative tensor factorization

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COMMON FATE MODEL FOR UNISON SOURCE SEPARATION 41

COMMON FATE MODEL FOR UNISON SOURCE SEPARATION

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Stoter, Fabian-Robert Liutkus, Antoine Badeau, Roland Edler, Bernd Magron, Paul Int Audio Labs Erlangen Erlangen Germany Inria Speech Proc Team Villers Les Nancy France Telecom ParisTech CNRS LTCI Inst Mines Telecom Paris France

ISBN: (纸本)9781479999880

In this paper we present a novel source separation method aiming to overcome the difficulty of modelling non-stationary signals. The method can be applied to mixtures of musical instruments with frequency and/or amplitude modulation, e.g. typically caused by vibrato. It is based on a signal representation that divides the complex spectrogram into a grid of patches of arbitrary size. These complex patches are then processed by a two-dimensional discrete Fourier transform, forming a tensor representation which reveals spectral and temporal modulation textures. Our representation can be seen as an alternative to modulation transforms computed on magnitude spectrograms. An adapted factorization model allows to decompose different time-varying harmonic sources based on their particular common modulation profile: hence the name Common Fate Model. The method is evaluated on musical instrument mixtures playing the same fundamental frequency (unison), showing improvement over other state-of-the-art methods.

关键词： Sound source separation Common Fate Model non-negative tensor factorization

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MUSIC GENRE CLASSIFICATION VIA TOPOLOGY PRESERVING non-negative tensor factorization AND SPARSE REPRESENTATIONS

MUSIC GENRE CLASSIFICATION VIA TOPOLOGY PRESERVING NON-NEGAT...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yannis Panagakis Constantine Kotropoulos Department of Informatics Aristotle University of Thessaloniki Box 451 Thessaloniki 54124 GREECE

ISBN: (纸本)9781424442959;9781424442966

Motivated by the rich, psycho-physiologically grounded properties of auditory cortical representations and the power of sparse representation-based classifiers, we propose a robust music genre classification framework. Its first pilar is a novel multilinear subspace analysis method that reduces the dimensionality of cortical representations of music signals, while preserving the topology of the cortical representations. Its second pilar is the sparse representation based classification, that models any test cortical representation as a sparse weighted sum of dictionary atoms, which stem from training cortical representations of known genre, by assuming that the representations of music recordings of the same genre are close enough in the tensor space they lie. Accordingly, the dimensionality reduction is made in a compatible manner to the working principle of the sparse-representation based classification. Music genre classification accuracy of 93.7percent and 94.93percent is reported on the GTZAN and the ISMIR2004 Genre datasets, respectively. Both accuracies outperform any accuracy ever reported for state of the art music genre classification algorithms applied to the aforementioned datasets.

关键词： Music genre classification topology preserving non-negative tensor factorization sparse representations topology sparse representation representations Music state of the art tensor Cortex of kidney Preserves sparse representation-based classification factorization operating principles Multiple signal classification dimensionality reduction

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Performance Assessment of Adaptive Controller for Switched Systems based on tensor Approach 27

Performance Assessment of Adaptive Controller for Switched S...

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27th Chinese Control and Decision Conference (CCDC)

作者： Jiang, Deng-Yin Hu, Li-Sheng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9781479970162

This paper mainly concentrates on the robust multiple model adaptive controller performance assessment for switched systems via using the tensor approach. The multiple model adaptive control scheme is employed for the switched control systems 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. The data-driven algorithms based on tensor space approach are derived for the calculation of performance measures by applying non-negative tensor factorization. It is shown that the controller performance can he obtained by the data processing method of non negative tensor factorization. Under some sufficient conditions, such as a strong finite time switching, or a finite number of the dynamical subprocess, the closed-loop subprocess controller performance can be improved obviously for multivariate switched systems. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed tensor approach by comparing the adaptive switching controller with other adaptive schemes or the single controller.

关键词： Multiple model adaptive control Robust adaptive controller performance assessment tensor Switched systems non-negative tensor factorization

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Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis

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Journal of Southeast University(English Edition) 2009年第3期25卷 346-350页

作者：彭森许飞云贾民平胡建中东南大学机械工程学院南京211189

Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.

关键词： non-negative tensor factorization sparseness feature extraction bispectrum gearbox

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Performance Assessment of Adaptive Controller for Switched Systems based on tensor Approach

Performance Assessment of Adaptive Controller for Switched S...

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第27届中国控制与决策会议

作者： Deng-Yin Jiang Li-Sheng Hu Department of Automation Shanghai Jiao Tong University

This paper mainly concentrates on the robust multiple model adaptive controller performance assessment for switched systems via using the tensor *** multiple model adaptive control scheme is employed for the switched control systems performance *** non-negative tensor factorization model is proposed to analyze the tensor data,which stems from uniqueness of low-rank decomposition of higher-order *** data-driven algorithms based on tensor space approach are derived for the calculation of performance measures by applying non-negative tensor *** is shown that the controller performance can be obtained by the data processing method of nonnegative tensor *** some sufficient conditions,such as a strong finite time switching,or a finite number of the dynamical subprocess,the closed-loop subprocess controller performance can be improved obviously for multivariate switched ***,a simulation example is presented to demonstrate the effectiveness of the proposed tensor approach by comparing the adaptive switching controller with other adaptive schemes or the single controller.

关键词： Multiple model adaptive control Robust adaptive controller performance assessment tensor Switched systems non-negative tensor factorization

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LOW-RANK APPROXIMATION BASED non-negative MULTI-WAY ARRAY DECOMPOSITION ON EVENT-RELATED POTENTIALS

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2014年第8期24卷 CP2-U20页

作者： Cong, Fengyu Zhou, Guoxu Astikainen, Piia Zhao, Qibin Wu, Qiang Nandi, Asoke K. Hietanen, Jari K. Ristaniemi, Tapani Cichocki, Andrzej Dalian Univ Technol Fac Elect Informat & Elect Engn Dept Biomed Engn Dalian Peoples R China Univ Jyvaskyla Dept Math Informat Technol SF-40351 Jyvaskyla Finland RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama Japan Univ Jyvaskyla Dept Psychol SF-40351 Jyvaskyla Finland Shandong Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China Brunel Univ Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Univ Tampere Sch Social Sci & Humanities Human Informat Proc Lab FIN-33101 Tampere Finland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.

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