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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shinozaki, Takahiro Kawahara, Tatsuya Kyoto Univ Acad Ctr Comp & Media Studies Kyoto Japan

ISBN: (纸本)9781424414833

In order to compensate for the weaknesses of the expectation maximization (EM) algorithm to over-training and to improve model performance for new data, we have recently proposed aggregated EM (Ag-EM) algorithm that introduces bagging-like approach in the framework of the EM algorithm and have shown that it gives similar improvements as cross-validation EM (CV-EM) over conventional EM. However, a limitation with the experiments was that the number of multiple models used in the aggregation operation or the ensemble size was fixed to a small value. Here, we investigate the relationship between the ensemble size and the performance as well as giving a theoretical discussion with the order of the computational cost. The algorithm is first analyzed using simulated data and then applied to large vocabulary speech recognition on oral presentations. Both of these experiments show that Ag-EM outperforms CV-EM by using larger ensemble sizes.

关键词： expectation maximization algorithm ensemble training bagging sufficient statistics hidden Markov model

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Counteracting Malicious Users in Cognitive Radio Networks over Imperfect Reporting Channels 6

Counteracting Malicious Users in Cognitive Radio Networks ov...

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6th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Du, Jun Xiang, Chaocan Guo, Daoxing Zhang, Bangning Zhang, Hailiang PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China PLA Unit 72959 Jinan 250031 Peoples R China

ISBN: (纸本)9781479973392

Spectrum sensing data falsification (SSDF) attack are serious threats to collaborative spectrum sensing (CSS) of cognitive radio networks (CRNs). In this paper, inspired by EM (expectation maximization) method, we propose a scheme to estimate the presences of primary user (PU) and the SUs' operating point parameters (false alarm and detection probabilities) iteratively. The key features of the proposed scheme is that, by using the estimated SUs' operating point parameters, the fusion center can estimate the presences of the PU, while the PU's state information is feedback to enhance the estimation accuracy of SUs' operating point parameters. Furthermore, our scheme can achieve a powerful capability of eliminating incorrect sensing reports, which can avoid over penalize the honest users who have random errors in reporting channels. The numerical result shows that, the proposed method can achieve higher malicious user detection accuracy than the existing reputation-based schemes, and can thus improve the CSS performance significantly.

关键词： Cognitive radio network collaborative spectrum sensing expectation maximization algorithm malicious secondary users' detection security

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FACTOR ANALYZED VOICE MODELS FOR HMM-BASED SPEECH SYNTHESIS

FACTOR ANALYZED VOICE MODELS FOR HMM-BASED SPEECH SYNTHESIS

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

作者： Kazumi, Kyosuke Nankaku, Yoshihiko Tokuda, Keiichi Nagoya Inst Technol Nagoya Aichi Japan

ISBN: (纸本)9781424442966

This paper describes factor analyzed voice models for realizing various voice characteristics in the HMM-based speech synthesis. The eigenvoice method can synthesize speech with arbitrary voice characteristics by interpolating representative HMM sets. However, the objective of PCA is to accurately reconstruct each speaker-dependent HMM set, and this is not equivalent to estimating models which represent training data accurately. To overcome this problem, we propose a general speech model which generates speech utterances with various voice characteristics directly. In the proposed method, the HMM states, factors representing voice characteristics and contextual decision trees are simultaneously optimized within a unified framework.

关键词： HMM-based speech synthesis eigenvoice factor analysis expectation maximization algorithm deterministic annealing EM algorithm

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A Note on Mixture Bivariate Model

A Note on Mixture Bivariate Model

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International Conference on Graphic and Image Processing (ICGIP)

作者： Cao, Hanwen Tian, Wei Deng, Chengzhi Nanchang Inst Technol Dept Sci Nanchang Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9780819489326

L. Sendur and I. W. Selesnick suggest four jointly non-Gaussian bivariate models to characterize the dependency between a coefficient and its parent, and respectively derive the corresponding MAP estimators based on noisy wavelet coefficients in detail in [6]. Among the four models, the second is a mixture model and it is quite complicated to evaluate parameters, so L. Sendur and I. W. Selesnick didn't give a concrete method. In this letter, a concrete mixture bivariate model will be described by drawing inspiration from Model 2. expectation maximization (EM) algorithm is employed to find the parameters of new model. The simulation results show that the values of PSNR have a bit improvement compared with Model 1. The results can be viewed as a supplementary of model 2 in [6].

关键词： Image denoising Mixture bivariate model Bayesian estimator expectation maximization algorithm

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An image threshold selection method based on the Burr distribution

An image threshold selection method based on the Burr distri...

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Conference on Optoelectronic Imaging and Multimedia Technology II

作者： Xie, Xiaohong Wu, Rongteng Minjiang Univ Dept Teaching Affairs Fuzhou 350108 Fujian Peoples R China Minjiang Univ Dept Comp Sci Fuzhou 350108 Peoples R China

ISBN: (纸本)9780819493132

It is important to accurately fit the unknown probability density functions of background or object. To solve this problem, the Burr distribution is introduced. Three-parameter Burr distribution can cover a wide range of distribution. The expectation maximization algorithm is used to deal with the estimation difficulty in the Burr distribution model. The expectation maximization algorithm starts from a set of selected appropriate parameters' initial values, and then iterates the expectation-step and maximization-step until convergence to produce result parameters. The experiment results show that the Burr distribution could depicts quite successfully the probability density function of a significant class of image, and comparatively the method has low computing complexity.

关键词： image segmentation threshold Burr distribution histogram expectation maximization algorithm probability density function distribution family computer vision

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SEMI-SUPERVISED LEARNING AND DISCOVERY OF UNKOWN STRUCTURES AMONG DATA: APPLICATION TO SATELLITE IMAGE ANNOTATION

SEMI-SUPERVISED LEARNING AND DISCOVERY OF UNKOWN STRUCTURES ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Blanchart, Pierre Datcu, Mihai CNRS UMR 5141 GET Telecom Paris LTCI F-75013 Paris France

ISBN: (纸本)9781424433940

In this paper, we present a semi-supervised method for auto-annotating image collections and discovering unknown structures among them. The approach relies on the existence of only a small training database of annotated examples. First, a fully-supervised algorithm using annotated samples is presented. Next, we introduce a semi-supervised procedure which allows us to incorporate unannotated samples and to infer the existence of unknown structures, that is, the existence of new image classes which are not represented in the training database. Finally, we present experimental results from a database of satellite images and briefly mention the possibility of reusing the presented approach as a basis for more complex systems such as Content Based Image Retrieval (CBIR) systems.

关键词： Image annotation Semi-supervised learning expectation maximization algorithm

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Semi-analytic Approach To Evaluate Performance of a Precoded Multiuser Cooperative Scheme

Semi-analytic Approach To Evaluate Performance of a Precoded...

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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Meghdadi, Hamid Cances, Jean-Pierre Meghdadi, Vahid Univ Limoges ENSIL F-87068 Limoges France

ISBN: (纸本)9781424480166

This paper addresses the problem of transmitting data to multiple mobile stations using a decode-and-forward strategy. Precoding vectors are used in relays to cancel out multiple access interference at the mobile stations. Statistical distribution of signal to noise ratio (SNR) is approximated by an expectation maximization algorithm. Based on this distribution, system performance is evaluated for low and high SNR. Simulation results confirm the analytic calculations and show that the maximum diversity advantage can be obtained, which is the product of the number of antennas at each relay by the number of relays minus the total number of system constraints.

关键词： cooperative communication decode-and-forward diversity diversity reception expectation maximization algorithm expectation-maximisation algorithm mobile stations mobile stations multiuser detection precoding precoding multiuser cooperative scheme semi-analytic approach signal to noise ratio statistical distribution statistical distributions

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Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

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IFAC-PapersOnLine 2017年第1期50卷 10778-10783页

作者： Yao L. Ge Z. Yuan X. Wang P. State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China School of Information Science and Engineering Central South University Changsha 410083 China School of Engineering Huzhou University Huzhou 313000 China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Bayes’ rule for quality estimation. However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. © 2017

关键词： Adaptive soft sensor expectation maximization algorithm Model integration Moving window Quality prediction Supervised latent factor analysis Weighted model

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Developing a Significant Nearest Neighbor Search Method for Effective Case Retrieval in a CBR System

Developing a Significant Nearest Neighbor Search Method for ...

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Spring Conference of the International-Association-of-Computer-Science-and-Information-Technology

作者： Tsai, Chieh-Yuan Chiu, Chuang-Cheng Yuan Ze Univ Dept Ind Engn & Management Chungli Taiwan

ISBN: (纸本)9780769536538

Case-based reasoning is a problem-solving technique commonly seen in artificial intelligence. A successful CBR system highly depends on how to design an effective case retrieval mechanism. The K-nearest neighbor (KNN) search method which selects the K most similar prior cases for a new case has been extensively used in the case retrieval phase of CBR. Although KNN can be simply implemented, the choice of the K value is quite subjective and wit] influence the performance of a CBR system. To eliminate the disadvantage, this research proposes a significant nearest neighbor (SNN) search method. In SNN, the probability density function of the dissimilarity distribution is estimated by the expectation maximization algorithm. Accordingly, the case selection can be conducted by determining whether the dissimilarity between a prior case and the new case is significant low based on the estimated dissimilarity distribution. The SNN search avoids human involvement in deciding the number of retrieved prior cases and makes the retrieval result objective and meaningful in statistics. The performance of the proposed SNN search method is demonstrated through a set of experiments.

关键词： nearest neighbor search statistical inference expectation maximization algorithm case-based reasoning

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Alternating minimization multigrid algorithms for transmission tomography

Alternating minimization multigrid algorithms for transmissi...

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Conference on Computational Imaging II

作者： O'Sullivan, JA Benac, J Washington Univ Dept Elect & Syst Engn Elect Syst & Signals Res Lab St Louis MO 63130 USA

ISBN: (纸本)0819452025

The problem of image formation for X-ray transmission tomography is formulated as a statistical inverse problem. The maximum likelihood estimate of the attenuation function is sought. Using convex optimization methods, maximizing the log-likelihood functional is equivalent to a double minimization of I-divergence, one of the minimizations being over the attenuation function. Restricting the minimization over the attenuation function to a coarse grid component forms the basis for a multigrid algorithm that is guaranteed to monotonically decrease the I-divergence at every iteration on every scale.

关键词： expectation maximization algorithm alternating minimization algorithms CT imaging transmission tomography multigrid methods

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