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Factor analysed hidden Markov models for speech recognition

COMPUTER SPEECH AND LANGUAGE 2004年第2期18卷 181-200页

作者： Rosti, AVI Gales, MJF Univ Cambridge Dept Engn Cambridge CB2 1PZ England

Recently various techniques to improve the correlation model of feature vector elements in speech recognition systems have been proposed. Such techniques include semi-tied covariance hidden Markov models (HMMs) and systems based on factor analysis. All these schemes have been shown to improve the speech recognition performance without dramatically increasing the number of model parameters compared to standard diagonal covariance Gaussian mixture HMMs. This paper introduces a general form of acoustic model, the factor analysed HMM. A variety of configurations of this model and parameter sharing schemes, some of which correspond to standard systems, were examined. An EM algorithm for the parameter optimisation is presented along with a number of methods to increase the efficiency of training. The performance of FAHMMs on medium to large vocabulary continuous speech recognition tasks was investigated. The experiments show that without elaborate complexity control an equivalent or better performance compared to a standard diagonal covariance Gaussian mixture HMM system can be achieved with considerably fewer parameters. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： model parameters speech recognition system Parameter optimization expectation-maximisation algorithm Eigenvector Speech recognition Covariance Hidden Markov models Diagonal

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Test of independence in a 2 x 2 contingency table with nonignorable nonresponse via constrained EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第12期52卷 5229-5241页

作者： Takai, Keiji Kano, Yutaka Osaka Univ Grad Sch Engn Sci Div Math Sci Osaka 5608531 Japan

Test of independence for 2 x 2 contingency tables with nonignorable nonresponses is discussed. Dependency assumption between two observed outcomes is required to achieve identification in many models with nonignorable nonresponses in the analysis of 2 x 2 contingency tables (e.g., [Ma, W.-Q., Geng, Z., Li, X.-T., 2003. Identification of nonresponse mechanisms for two-way contingency tables. Behaviormetrika 30, 125-144]). The assumption is, however, violated under the null hypothesis when implementing the test of independence. In this article, we introduce a new simple assumption to achieve identification. The assumption involves pre-specified parameters. EM algorithms for finding the MLE are numerically unstable when there are nonlinear constraints, which are created by models treating nonignorable nonresponses. In the analysis of contingency tables, estimated values often fall outside the admissible region. We propose a new EM type algorithm to stably calculate the constrained MLE, and apply it to make the test of independence for a real data set (crime data). We compare empirical performance among several testing procedures for independence. It turns out that the new EM type algorithm works well to calculate the MLE, and that the nonignorable model with the correctly specified parameters performs best while the conventional chi-square test of independence works fairly well. (c) 2008 Elsevier B.V. All rights reserved.

关键词： contingency expectation-maximisation algorithm maximum likelihood estimation nonlinear constraint Testing Procedures Nonresponse

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Topic-based hierarchical Bayesian linear regression models for niche items recommendation

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JOURNAL OF INFORMATION SCIENCE 2019年第1期45卷 92-104页

作者： Liu, Yezheng Xiong, Qiang Sun, Jianshan Jiang, Yuanchun Silva, Thushari Ling, Haifeng Hefei Univ Technol Sch Management Tunxi Rd 193 Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Mak Hefei Anhui Peoples R China Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

A vital research concern for a personalised recommender system is to target items in the long tail. Studies have shown that sales of the e-commerce platform possess a long-tail character, and niche items in the long tail are challenging to be involved in the recommendation list. Since niche items are defined by the niche market, which is a small market segment, traditional recommendation algorithms focused more on popular items promotion and they do not apply to the niche market. In this article, we aim to find the best users for each niche item and proposed a topic-based hierarchical Bayesian linear regression model for niche item recommendation. We first identify niche items and build niche item subgroups based on descriptive information of items. Moreover, we learn a hierarchical Bayesian linear regression model for each niche item subgroup. Finally, we predict the relevance between users and niche items to provide recommendations. We perform a series of validation experiments on Yahoo Movies dataset and compare the performance of our approach with a set of representative baseline recommender algorithms. The result demonstrates the superior performance of our recommendation approach for niche items.

关键词： expectation-maximisation algorithm hierarchical Bayesian linear regression models niche item recommendation personalised recommendation

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Bi-ISAR sparse imaging algorithm with complex Gaussian scale mixture prior

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IET RADAR SONAR AND NAVIGATION 2019年第12期13卷 2202-2211页

作者： Zhu, Xiaoxiu Shi, Lin Guo, Baofeng Hu, Wenhua Shang, Chaoxuan Han, Ning Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Campus Shijiazhuang 050003 Hebei Peoples R China PLA 32181 Unit Shijiazhuang 050003 Hebei Peoples R China

The performance of both autofocusing and imaging resolution degrades using the traditional autofocusing and range-Doppler algorithm for bistatic inverse synthetic aperture radar (Bi-ISAR) with sparse apertures. A Bi-ISAR sparse imaging algorithm based on complex Gaussian scale mixture (CGSM) prior is proposed to jointly achieve the high-resolution imaging and autofocusing. First, a sparse basis matrix with the time-varying bistatic angle is constructed to represent the sparse echo data and the Bi-ISAR joint with autofocusing imaging model is established based on compressed sensing from sparse apertures. Second, the elements of the target image and the noise are assumed to be a CGSM prior with Gaussian distribution, respectively. Finally, the sparse image reconstruction and phase autofocusing are accomplished by the variational Bayesian expectation maximisation method. The proposed algorithm with the full Bayesian inference can obtain a well-focused image without manual adjustments of regularisation parameters. Meanwhile, it can avoid the local minimum and structural errors, due to utilising the statistical information of a posterior. Simulated results of electromagnetic numerical data verify the superiority of the algorithm in autofocusing, sparse imaging and noise suppression performance.

关键词： radar imaging synthetic aperture radar Bayes methods Gaussian distribution image resolution image reconstruction expectation-maximisation algorithm radar resolution image denoising compressed sensing echo sparse matrices Bi-ISAR sparse imaging algorithm high-resolution imaging sparse basis matrix sparse echo data autofocusing imaging model sparse apertures sparse image reconstruction phase autofocusing bistatic inverse synthetic aperture radar range-Doppler algorithm complex Gaussian scale mixture prior CGSM prior time-varying bistatic angle compressed sensing Gaussian distribution variational Bayesian expectation maximisation method Bayesian inference statistical a posterior information electromagnetic numerical data noise suppression performance

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HS remote sensing image restoration using fusion with MS images by EM algorithm

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IET SIGNAL PROCESSING 2017年第1期11卷 95-103页

作者： Ansari, Amir Danyali, Habibollah Helfroush, Mohammad Sadegh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71dB for Moffett database and 16.26dB for HYDICE database.

关键词： remote sensing image restoration image fusion hyperspectral imaging expectation-maximisation algorithm image denoising wavelet transforms Gaussian processes mixture models geophysical image processing HS remote sensing image restoration MS images EM algorithm mineral exploration agricultural application hyperspectral image quality degraded HS image restoration multispectral observation HS image-MS image fusion maximum a posteriori estimation expectation maximisation algorithm deblurring denoising spatial domain nonoverlapping blocks wavelet domain multinormal model Gaussian scale mixture airborne visible-infrared imaging spectrometer HS digital imagery collection experiment HYDICE database signal-to-noise ratio Moffett database

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Single-cell and multi-cell performance analysis of OFDM index modulation

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IET COMMUNICATIONS 2017年第7期11卷 1021-1027页

作者： Wu, Shangbin Nekovee, Maziar Samsung R&D Inst UK Staines Upon Thames England

This study addresses the achievable rate of single cell and sum rate of multi-cell orthogonal frequency division multiplexing (OFDM) index modulation (IM). The single-cell achievable rate of OFDM-IM with Gaussian input is calculated using a multi-ary symmetric channel. Then, the cumulative distribution function of multi-cell OFDM-IM is investigated by stochastic geometry. Furthermore, it is proved in this study that the probability density function of noise plus inter-cell interference in multicell OFDM-IM with quadrature-amplitude modulation follows a mixture of Gaussians (MoGs) distribution. Next, parameters of the MoG distribution are estimated using a simplified expectation maximisation algorithm. Upper bound of sum rates of multi-cell OFDM-IM is derived. Furthermore, analytic and simulated results are compared and discussed.

关键词： cellular radio OFDM modulation Gaussian distribution stochastic processes radiofrequency interference quadrature amplitude modulation expectation-maximisation algorithm OFDM index modulation multicell performance analysis OFDM index modulation singlecell analysis multicell orthogonal frequency division multiplexing index modulation sum rate OFDM-IM single-cell achievable rate Gaussian input multiary symmetric channel multicell OFDM-IM cumulative distribution function stochastic geometry noise plus intercell interference probability density function quadrature amplitude modulation mixture of Gaussian distribution simplified expectation maximisation algorithm MoG distribution estimation multicell OFDM-IM sum rate upper bound

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Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

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IET CONTROL THEORY AND APPLICATIONS 2015年第11期9卷 1683-1691页

作者： Sutarto, Herman Yoseph Boel, Rene K. Joelianto, Endra Univ Ghent EESA SYSTeMS Res Grp B-9052 Zwijnaarde Belgium Bandung Inst Technol Instrumentat & Control Res Grp Bandung 40132 Indonesia

This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation - free flowing, congested or faulty - making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator.

关键词： parameter estimation Markov processes autoregressive processes expectation-maximisation algorithm matrix algebra particle filtering (numerical methods) road traffic stochastic hybrid model urban traffic flow estimation data-based approach urban traffic network signalised intersections mode-dependent first-order autoregressive stochastic process AR stochastic process mode switching first-order Markov chain jump Markov process expectation-maximisation technique EM technique transition matrix estimation smoothed inference algorithms online particle filter EM parameter estimation time-window shift technique adaptive traffic flow state estimator adaptive model-based filter feedback control traffic lights traffic flow rate

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EM enhancement of 3D head pose estimated by point at infinity

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IMAGE AND VISION COMPUTING 2007年第12期25卷 1864-1874页

作者： Wang, Jian-Gang Sung, Eric Inst Infocomm Res Singapore 119613 Singapore Nanyang Technol Univ Singapore 639798 Singapore

Head pose estimation is a key task for visual surveillance, HCI and face recognition applications. In this paper, a new approach is proposed for estimating 3D head pose from a monocular image. The approach assumes the full perspective projection camera model. Our approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of a certain vanishing point to determine the pose of human faces. To achieve this, eye-lines, formed from the far and near eye corners, and mouth-line of the mouth corners are assumed parallel in 3D space. Then the vanishing point of these parallel lines found by the intersection of the eye-line and mouth-line in the image can be used to infer the 3D orientation and location of the human face. In order to deal with the variance of the facial model parameters, e.g. ratio between the eye-line and the mouth-line, an EM framework is applied to update the parameters. We first compute the 3D pose using some initially learnt parameters (such as ratio and length) and then adapt the parameters statistically for individual persons and their facial expressions by minimizing the residual errors between the projection of the model features points and the actual features on the image. In doing so, we assume every facial feature point can be associated to each of features points in 3D model with some a posteriori probability. The expectation step of the EM algorithm provides an iterative framework for computing the a posterori probabilities using Gaussian mixtures defined over the parameters. The robustness analysis of the algorithm on synthetic data and some real images with known ground-truth are included. (C) 2006 Elsevier B.V. All rights reserved.

关键词： head pose vanishing point expectation-maximisation algorithm model adaptation

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Multimodal supervised image translation

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ELECTRONICS LETTERS 2019年第4期55卷 190-191页

作者： Ruan, Congcong Chen, Dihu Hu, Haifeng Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

Multimodal image-to-image translation is a class of vision and graphics problems where the goal is to learn a one-to-many mapping between the source domain and target domain. Given an image in the source domain, the model aims to produce as many diverse results as possible. It is an important and challenging problem in the task of image translation. To this end, recent works utilise Gaussian vectors to produce diverse results but with a small difference. It is because of the special probabilistic nature of Gaussian distribution. In this work, the authors propose linearly distributed latent codes instead of conventional Gaussian vectors, which control the style of generated images. Taking advantage of linear distribution, their model can produce much more diverse results and outperform the state-of-the-art baselines in terms of diversity. Qualitative and quantitative comparisons against baselines demonstrate the effectiveness and superiority of their method.

关键词： expectation-maximisation algorithm Gaussian distribution learning (artificial intelligence)

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Modelling of extended objects using sparse multi-aspect High Range Resolution radar data set

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IET RADAR SONAR AND NAVIGATION 2011年第7期5卷 756-768页

作者： Fasoula, A. van Genderen, P. Delft Univ Technol IRCTR MTS Radar Delft Netherlands

The two-dimensional (2D) geometry of extended objects is modelled as a discrete constellation of a small number of scattering centres. Each scattering centre is characterised by its 2D location and extension, as well as by its observability from aspect angles spanning a wide angular interval. The considered input information of the modelling consists of high range resolution (HRR) data sets, which are measured by a network of scanning surveillance radars from a limited set of aspect angles of the object. During the short time-on-target of the antennas within each radar scan, inverse synthetic aperture radar (ISAR) imaging is performed. The expectation-maximisation (ER) algorithm is applied in order to fit each ISAR image to a Gaussian mixture model (GMM), pertaining to the aspect angle applicable to the respective radar. A merging algorithm for clustering all the partial GMMs into a unique 2D model of the extended object is designed. Cluster validation criteria are applied for correct alignment of the multi-radar multi-scan object model estimates. A Mixture of Gaussians classifier (MOGC), which is based on representation of each object by the estimated 2D model, is discussed. Results from application of the algorithms to two measured HRR radar data sets are presented.

关键词： expectation-maximisation algorithm 2D geometry radar imaging ER algorithm sparse multiaspect high range resolution radar data set multiradar multiscan object model cluster validation criteria scanning surveillance radar Optical, image and video signal processing ISAR imaging MOGC discrete constellation Combinatorial mathematics Radar equipment, systems and applications search radar GMM HRR radar data set inverse synthetic aperture radar imaging Gaussian processes two-dimensional geometry synthetic aperture radar geometry Gaussian mixture model radar antennas wide angular interval antennas Other topics in statistics

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