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A nonparametric bayesian model for system identification based on a super-Gaussian distribution

ELECTRONICS AND COMMUNICATIONS IN JAPAN 2019年第7期102卷 32-40页

作者： Tanji, Hiroki Murakami, Takahiro Kamata, Hiroyuki Meiji Univ Sch Sci & Technol Dept Elect & Bioinformat JSPS Res Fellowship Young Specialists DC2 Kawasaki Kanagawa Japan Meiji Univ Sch Sci & Technol Dept Elect & Bioinformat Kawasaki Kanagawa Japan

In the acoustic signal processing applications of finite impulse response (FIR) system identification, it is important to develop the identification method that is robust to super-Gaussian noises. Moreover, the identification method that estimates the FIR coefficients and the order of the unknown system is required, because the order of the unknown system is unavailable in advance. Therefore, in this paper, we propose a nonparametric bayesian (NPB) model for FIR system identification using a super-Gaussian likelihood and the beta-Bernoulli process. In the proposed NPB model, we employ the hyperbolic secant distribution for the likelihood function. Then, we derive the inference algorithm to simultaneously estimate the FIR coefficients and the order of the unknown system. Our inference algorithm based on a hybrid inference approach combining the majorization-minimization (MM) algorithm and the Gibbs sampler. The simulation results suggest that the proposed method outperforms the conventional identification algorithms in a super-Gaussian noise environment.

关键词： beta-Bernoulli process Gibbs sampler majorization-minimization algorithm nonparametric bayesian model super-Gaussian distribution system identification

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nonparametric bayesian models for AR and ARX Identification 12

Nonparametric Bayesian Models for AR and ARX Identification

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12th IEEE International Colloquium on Signal Processing & its Applications (CSPA)

作者： Tanji, Hiroki Tanaka, Ryo Murakami, Takahiro Ishida, Yoshihisa Meiji Univ Sch Sci & Technol Kanagawa 2148571 Japan Org Strateg Coordinat Res & Intellectual Property Kanagawa 2148571 Japan

ISBN: (纸本)9781467387804

In this paper, we propose nonparametric bayesian (NPB) models for autoregressive (AR) and autoregressive exogenous (ARX) identification. In the proposed AR model, we assumed that its coefficients are given by the Bernoulli process. Then, the proposed AR model was extended to the NPB model for ARX identification using two independent Bernoulli processes. The posterior distributions of the proposed models were investigated using the Gibbs sampler, and the coefficients and the order of the systems were simultaneously estimated. The effectiveness of the proposed methods was confirmed using numerical simulations.

关键词： AR model ARX model system identification nonparametric bayesian model Bernoulli process Gibbs sampler

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Fully bayesian speaker clustering based on hierarchically structured utterance-oriented Dirichlet process mixture model

Fully Bayesian speaker clustering based on hierarchically st...

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13th Annual Conference of the International-Speech-Communication-Association

作者： Tawara, Naohiro Ogawa, Tetsuji Watanabe, Shinji Nakamura, Atsushi Kobayashi, Tetsunori Waseda Univ Tokyo Japan

ISBN: (纸本)9781622767595

We have proposed a novel speaker clustering method based on a hierarchically structured utterance-oriented Dirichlet process mixture model. In the proposed method, the number of speakers can be determined from the given data using a nonparametric bayesian manner and intra-speaker variability is successfully handled by multi-scale mixture modeling. Experimental result showed that the proposed method is computationally-efficient and effective in speaker clustering. The proposed method significantly improve the accuracy of speaker clustering systems as compared with the conventional method, particularly for the case in which the number of utterances varied from speaker to speaker.

关键词： Speaker clustering nonparametric bayesian model Gibbs sampling utterance-oriented Dirichlet process mixture model

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Underground Pipeline Mapping Based on Dirichlet Process Mixture model

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IEEE ACCESS 2020年 8卷 118114-118124页

作者： Wu, Qingyuan Zhou, Xiren Chen, Huanhuan Univ Sci & Technol China USTC Sch Comp Sci & Technol UBRI Hefei 230027 Peoples R China

Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines. In this paper, an automatic pipeline mapping model, the Dirichlet Process Pipeline Mapping model (DPPMM), is introduced with GPR and Global Position System (GPS) data as input. By combining the GPR and GPS the position, direction, depth and size of pipelines could be estimated. The number of buried pipelines in the detection site could be automatically estimated with the benefit of DPPMM, without any prior knowledge. By adopting this model, the probabilities of each survey point belonging to each pipeline are calculated, and the pipeline directions and locations are also estimated. The experimental results demonstrate that this model could obtain more accurate pipeline maps than other state-ofthe-art algorithms in various experimental settings.

关键词： Ground penetrating radar (GPR) pipeline mapping clustering nonparametric bayesian model

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Unsupervised Segmentation of Human Motion Data Using a Sticky Hierarchical Dirichlet Process-Hidden Markov model and Minimal Description Length-Based Chunking Method for Imitation Learning

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ADVANCED ROBOTICS 2011年第17期25卷 2143-2172页

作者： Taniguchi, Tadahiro Hamahata, Keita Iwahashi, Naoto Ritsumeikan Univ Shiga 5258577 Japan Natl Inst Informat & Commun Technol Seika Kyoto 6190289 Japan

We propose an unsupervised motion segmentation method for enabling a robot to imitate and perform various unit motions by observing unsegmented human motion. Natural unsegmented human motion data contain various types of unit motions, such as 'waving good-bye', 'walking' and 'throwing a ball'. A robot has to segment the data and extract unit motions from the data to imitate the motions. In previous work, an ergodic hidden Markov model (HMM) was used to model unsegmented human motion. However, there are two main problems with the classical use of this model. (i) Setting an appropriate number of hidden states is difficult because how complex the motions contained in the learning data are and how many there are unknown. (ii) We did not have an effective chunking method that could chunk elemental motions into meaningful unit motions without being captured by local minima. To overcome these problems, we developed an unsupervised motion segmentation method for imitation learning using a sticky hierarchical Dirichlet process (HDP)-HMM, a nonparametric bayesian model, and an unsupervised chunking method based on a Gibbs sampler and the minimal description length (MDL) principle of imitation learning of unsegmented human motion. We developed this chunking method to work with the sticky HDP-HMM and extract unit human motions. We conducted several experiments to evaluate this method. The proposed method could extract unit motions from unsegmented human motion data. The sticky HDP-HMM can be used to model unsegmented human motion more accurately than with a conventional HMM and simultaneously estimate the number of hidden states. We also evaluated the dependency of the HDP-HMM on the hyperparameters of the model. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011

关键词： Unsegmented human motion imitation learning nonparametric bayesian model sticky HDP-HMM ergodic HMM

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Statistical Parametric Speech Synthesis Based on Gaussian Process Regression

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2014年第2期8卷 173-183页

作者： Koriyama, Tomoki Nose, Takashi Kobayashi, Takao Tokyo Inst Technol Interdisciplinary Grad Sch Sci & Engn Dept Informat Proc Yokohama Kanagawa 2268502 Japan

This paper proposes a statistical parametric speech synthesis technique based on Gaussian process regression (GPR). The GPR model is designed for directly predicting frame-level acoustic features from corresponding information on frame context that is obtained from linguistic information. The frame context includes the relative position of the current frame within the phone and articulatory information and is used as the explanatory variable in GPR. Here, we introduce cluster-based sparse Gaussian processes (GPs), i.e., local GPs and partially independent conditional (PIC) approximation, to reduce the computational cost. The experimental results for both isolated phone synthesis and full-sentence continuous speech synthesis revealed that the proposed GPR-based technique without dynamic features slightly outperformed the conventional hidden Markov model (HMM)-based speech synthesis using minimum generation error training with dynamic features.

关键词： Gaussian process regression nonparametric bayesian model partially independent conditional (PIC) approximation sparse Gaussian processes statistical speech synthesis

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Overlapping community detection for count-value networks

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2019年第1期9卷 1页

作者： Yu, QianCheng Yu, ZhiWen Wang, Zhu Wang, XiaoFeng Wang, YongZhi Northwestern Polytech Univ Sch Comp Sci Dongxiang Rd Xian 710072 Peoples R China Northwestern Polytech Univ Shanxi Prov Key Lab Embedded Syst Dongxiang Rd Xian 710072 Peoples R China North Minzu Univ Coll Comp Sci Yinchuan Peoples R China Jilin Univ Coll Instruments Changchun Jilin Peoples R China

Detecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z;thus, provide a flexible nonparametric bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results.

关键词： Overlapping community detection Count-value networks Generative network model nonparametric bayesian model Indian buffet process Inference quality estimation

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nonparametric Hierarchical bayesian models for Positive Data Clustering Based on Inverted Dirichlet-Based Distributions

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IEEE ACCESS 2019年 7卷 83600-83614页

作者： Fan, Wentao Bouguila, Nizar Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Fujian Peoples R China Concordia Univ CIISE Montreal PQ H3G 2W1 Canada

In this paper, we propose nonparametric hierarchical bayesian models based on two inverted Dirichlet-based distributions and Pitman-Yor process for positive data features clustering. The choice of the inverted Dirichlet and the generalized inverted Dirichlet distributions is motivated by their flexibility and modeling capabilities when dealing with this kind of data, while deploying the Pitman-Yor process prior is justified by its power-law behavior, which makes it a natural choice in real-life application compared with Dirichlet processes for instance. The inference for the resulting models takes into account the challenging problem of feature weighting/selection and is conducted under a bayesian setting by means of the recently proposed stochastic variational Bayes technique. The efficacy and merits of the proposed approaches are examined using the synthetic data and a challenging real-life application that concerns video background subtraction.

关键词： Clustering mixture models inverted Dirichlet nonparametric bayesian model stochastic variational inference

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A dependent Dirichlet process model for survival data with competing risks

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LIFETIME DATA ANALYSIS 2021年第1期27卷 156-176页

作者： Shi, Yushu Laud, Purushottam Neuner, Joan Univ Missouri Middlebush Hall Columbia MO 65201 USA Med Coll Wisconsin CAPS 8701 Watertown Plank Rd Milwaukee WI 53226 USA

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models' performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package "DPWeibull" implementing all of the proposed methods is available at CRAN.

关键词： Survival analysis Competing risks nonparametric bayesian model Time-dependent covariate

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Functional clustering by bayesian wavelet methods

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2006年第2期68卷 305-332页

作者： Ray, S Mallick, B Texas A&M Univ Dept Stat College Stn TX 77843 USA

We propose a nonparametric Bayes wavelet model for clustering of functional data. The wavelet-based methodology is aimed at the resolution of generic global and local features during clustering and is suitable for clustering high dimensional data. Based on the Dirichlet process, the nonparametric Bayes model extends the scope of traditional Bayes wavelet methods to functional clustering and allows the elicitation of prior belief about the regularity of the functions and the number of clusters by suitably mixing the Dirichlet processes. Posterior inference is carried out by Gibbs sampling with conjugate priors, which makes the computation straightforward. We use simulated as well as real data sets to illustrate the suitability of the approach over other alternatives.

关键词： functional clustering mixture of Dirichlet processes nonparametric bayesian model wavelets

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