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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Fakhry, Mahmoud Svaizer, Piergiorgio Omologo, Maurizio University of Trento via Sommarive 5 Trento Italy Center of Information Technology Fondazione Bruno Kessler - Irst via Sommarive 18 Trento Italy

ISBN: (纸本)9781467369978

This work proposes a solution to the problem of under-determined audio source separation using pre-trained redundant source-based prior information. In local Gaussian modeling of a mixing process, an observed mixture is modeled by a Gaussian distribution parameterized by source variances and spatial covariance matrices. The separation is performed by estimating the parameters, and applying Wiener filtering on the observed mixture. We propose, in a training phase, to build a redundant library of spectral basis matrices of all probable source power spectra, applying non-negative tensor factorization (NTF). In the testing phase, the matrices that match the observed mixture are detected using NTF. With the help of the detected matrices, a maximum likelihood algorithm is proposed in order to iteratively estimate the parameters of the model, exploiting the spatial redundancy of the observed mixture and using NTF. The proposed algorithm proves more flexibility and efficiency with respect to a baseline algorithm used as a reference. © 2015 IEEE.

关键词： audio source separation model parameters non-negative tensor factorization redundant library Spectral bases

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

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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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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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BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY non-negative tensor factorization FROM EEG COLLECTED BY LOW-DENSITY ARRAY

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2012年第6期22卷 1250025-1250025页

作者： Cong, Fengyu Anh Huy Phan Zhao, Qibin Huttunen-Scott, Tiina Kaartinen, Jukka Ristaniemi, Tapani Lyytinen, Heikki Cichocki, Andrzej 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

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets.

关键词： EEG event-related potential mismatch negativity multi-domain feature 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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non-negative Multiple tensor factorization

Non-negative Multiple Tensor Factorization

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IEEE 13th International Conference on Data Mining (ICDM)

作者： Takeuchi, Koh Tomioka, Ryota Ishiguro, Katsuhiko Kimura, Akisato Sawada, Hiroshi NTT Commun Sci Labs Kyoto Japan Toyota Technol Inst Chicago IL USA

non-negative tensor factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In this paper, we propose non-negative Multiple tensor factorization (NMTF), which factorizes the target tensor and auxiliary tensors simultaneously. Auxiliary data tensors compensate for the sparseness of the target data tensor. The factors of the auxiliary tensors also allow us to examine the target data from several different aspects. We experimentally confirm that NMTF performs better than NTF in terms of reconstructing the given data. Furthermore, we demonstrate that the proposed NMTF can successfully extract spatio-temporal patterns of people's daily life such as leisure, drinking, and shopping activity by analyzing several tensors extracted from online review data sets.

关键词： Machine Learning non-negative tensor factorization Spatio-Temporal Pattern

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non-negative tensor Representation and Unsupervised Classification of Object Pose in Continuous Image Sequences 4th

Non-negative Tensor Representation and Unsupervised Classifi...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Ying, Zhihao Zhao, Yang Pei, Jihong Yang, Xuan Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819603534;9789819603541

The analysis of video data in machine learning classification tasks has become a significant topic in both research and application areas. The accurate classification of video data by frame sets, particularly when the content contains objects that are in a state of dynamic change, represents a significant and complex undertaking. With regard to the temporal phase of video, the thesis proposes an unsupervised classification method based on non-negative tensor factorization (NTF). In order to transform the video data into low-dimensional data that can be classified by unsupervised clustering, the non-negative tensor factorization method is used to reduce the dimension of the input tensor, resulting in the generation of a non-negative rank-1 tensor combination. Following dimensionality reduction, the extracted features are divided into clusters through Mean Shift, thereby facilitating classification and recognition of video sequences. Experiments on the Coil-100 dataset of item image sequences at different angles and other dynamic pose-changing videos demonstrate the effectiveness of the method.

关键词： Video timing division non-negative tensor factorization Unsupervised learning

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Computing non-negative tensor factorizations

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OPTIMIZATION METHODS & SOFTWARE 2008年第4期23卷 631-647页

作者： Friedlander, Michael P. Hatz, Kathrin Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada Heidelberg Univ Interdisciplinary Ctr Sci Comp D-6900 Heidelberg Germany

non-negative tensor factorization (NTF) is a technique for computing a parts-based representation of high-dimensional data. NTF excels at exposing latent structures in datasets, and at finding good low-rank approximations to the data. We describe an approach for computing the NTF of a dataset that relies only on iterative linear-algebra techniques and that is comparable in cost to the non-negative matrix factorization (NMF). (The better-known NMF is a special case of NTF and is also handled by our implementation.) Some important features of our implementation include mechanisms for encouraging sparse factors and for ensuring that they are equilibrated in norm. The complete MATLAB software package is available under the GPL license.

关键词： N-dimensional arrays tensors non-negative tensor factorization alternating least-squares block Gauss-Seidel sparse solutions regularization non-negative least-squares

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Variational auto-encoder based Bayesian Poisson tensor factorization for sparse and imbalanced count data

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DATA MINING AND KNOWLEDGE DISCOVERY 2021年第2期35卷 505-532页

作者： Jin, Yuan Liu, Ming Li, Yunfeng Xu, Ruohua Du, Lan Gao, Longxiang Xiang, Yong Monash Univ Fac Informat Technol Melbourne Vic 3800 Australia Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia Sandstone Pty Ltd 32-42 Barker St Kingsford NSW 2032 Australia

non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models can derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules for the posterior parameters. They also fail to share the update information to better cope with the data sparsity. Moreover, these models are not endowed with a component that handles the imbalance in count data values. In this paper, we propose a novel variational auto-encoder framework called VAE-BPTF which addresses the above issues. It uses multi-layer perceptron networks to encode and share complex update information. The encoded information is then reweighted per data instance to penalize common data values before aggregated to compute the posterior parameters for the latent factors. Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values. It also outperformed current models in both reconstruction errors and latent factor (semantic) coherence across five real-world datasets. Furthermore, the latent factors inferred by VAE-BPTF are perceived to be meaningful and coherent under a qualitative analysis.

关键词： non-negative tensor factorization Variational auto-encoders Neural networks Latent variable modelling Count data

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Brain CT image database building for computer-aided diagnosis using content-based image retrieval

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INFORMATION PROCESSING & MANAGEMENT 2011年第2期47卷 176-185页

作者： Yuan, Kehong Tian, Zhen Zou, Jiying Bai, Yanling You, Qingshan Tsinghua Univ Grad Sch Shenzhen Beijing Peoples R China Harbin Med Coll Tumor Hosp Harbin 150080 Peoples R China

Content-based image retrieval for medical images is a primary technique for computer-aided diagnosis. While it is a premise for computer-aided diagnosis system to build an efficient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images are securely acquired along with relevant diagnosis reports and then cleansed, validated and enhanced. Then some sophisticated image processing algorithms including image normalization and registration are applied to make sure that only corresponding anatomy regions could be compared in image matching. A vector of features is extracted by non-negative tensor factorization and associated with each image, which is essential for the content-based image retrieval. Our experiments prove the efficiency and promising prospect of this database building method for computer-aided diagnosis system. The brain CT image database we built could provide radiologists with a convenient access to retrieve pre-diagnosed, validated and highly relevant examples based on image content and obtain computer-aided diagnosis. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Content-based image retrieval Brain CT image Database management Computer-aided diagnosis non-negative tensor factorization

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