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13th International KES Conference on Intelligent Decision Technologies (KES-IDT)

作者： Ohishi, Mineaki Okamura, Kensuke Itoh, Yoshimichi Yanagihara, Hirokazu Hiroshima Univ Educ & Res Ctr Artificial Intelligence & Data Inn Hiroshima 7300053 Japan Tokyo Kantei Co Ltd Shinagawa 1410021 Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima 7398526 Japan

ISBN: (纸本)9789811627651;9789811627644

In linear regression, a non-linear structure can be naturally considered by transforming quantitative explanatory variables to categorical variables. Moreover, smaller categories make estimation more flexible. However, a trade-off between flexibility of estimation and estimation accuracy occurs because the number of parameters increases for smaller categorizations. We propose an estimation method wherein parameters for categories with equal effects are equally estimated via generalized fused Lasso. By such a method, it can be expected that the degrees of freedom for the model decreases, flexibility of estimation and estimation accuracy are maintained, and categories of explanatory variables are optimized. We apply the proposed method to modeling of apartment rents in Tokyo's 23 wards.

关键词： coordinate descent algorithm Generalized fused lasso Linear model Real estate data analysis

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Penalized log-density estimation using Legendre polynomials

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2020年第11期49卷 2844-2860页

作者： Lee, JungJun Jhong, Jae-Hwan Cho, Young-Rae Kim, SungHwan Koo, Ja-Yong Korea Univ Dept Stat Seoul South Korea ETUDE Corp Seoul South Korea Konkuk Univ Dept Appl Stat Seoul South Korea

In this article, we present a penalized log-density estimation method using Legendre polynomials with l(1) penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC). In particular, we derive a numerical solution to the maximum tuning parameter lambda(max) which leads to all zero coefficients and practically facilitates searching the optimal tuning parameter. Extensive simulation studies clearly show that our proposed estimator is computationally competitive with other existing nonparametric density estimators (e.g., kernel, kernel smooth and logspline estimators) benchmarked by the mean integrated squared errors (MISE) and the mean integrated absolute error (MIAE) under the experiment scenario of separated bimodal models in regard to the true density function. With an application to Old Faithful geyser data, our proposed method is found to effectively perform density estimation.

关键词： Penalized log-density estimation nonparametric density estimation Legendre polynomial basis coordinate descent algorithm l(1) penalty maximum tuning parameter

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Penalized logspline density estimation using total variation penalty

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2021年 153卷 107060-107060页

作者： Bak, Kwan-Young Jhong, Jae-Hwan Lee, JungJun Shin, Jae-Kyung Koo, Ja-Yong Korea Univ Dept Stat Seoul 02841 South Korea Chungbuk Natl Univ Dept Informat Stat Chungcheongbuk Do 28644 South Korea LG Display AI BigData Anal Team 245 LG Ro Paju Si 10845 Gyeonggi Do South Korea

We study a penalized logspline density estimation method using a total variation penalty. The B-spline basis is adopted to approximate the logarithm of density functions. Total variation of derivatives of splines is penalized to impart a data-driven knot selection. The proposed estimator is a bonafide density function in the sense that it is positive and integrates to one. We devise an efficient coordinate descent algorithm for implementation and study its convergence property. An oracle inequality of the proposed estimator is established when the quality of fit is measured by the Kullback-Leibler divergence. Based on the oracle inequality, it is proved that the estimator achieves an optimal rate of convergence in the minimax sense. We also propose a logspline method for the bivariate case by adopting the tensor-product B-spline basis and a two-dimensional total variation type penalty. Numerical studies show that the proposed method captures local features without compromising the global smoothness. (C) 2020 Elsevier B.V. All rights reserved.

关键词： B-spline coordinate descent algorithm Kullback-Leibler divergence Optimal convergence rate Oracle inequality

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A new methodology for evaluating profile and position errors of blade based on parameter priority

A new methodology for evaluating profile and position errors...

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International Conference on Optical Instruments and Technology (OIT) - Optoelectronic Measurement Technology and Systems

作者： Hao, Junjie Huang, Junhui Wang, Zhao Gao, Janmin Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Western China Inst Qual Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9781510636576

As one of the core components of a turbine, the quality of the blade manufacturing has a strong impact on the energy conversion efficiency of the turbine, where the key technology of quality evaluation of blades is point cloud registration. However, with the application of structured light three-dimensional measurement technology in full profile measurement, the typical point cloud registration methods only focus on the minimization of surface profile error, ignoring the position error relative to reference datum, which can easily lead to the misjudgment of qualified blades. In this paper, a new blade error evaluation method is presented to register the point cloud data scanned from a physical blade to its theoretical CAD model, which fits the two surfaces based on parameter priority. Firstly, qualified blades are quickly selected after global fine registration using the best-fit algorithm. Subsequently, based on the priority of position error parameters, the coordinate descent algorithm combined with the minimum zone criterion is adopted for local fine registration, which guarantees accurate evaluation results. Finally, the shape and position error of the actual blade is obtained accurately by calculating transformation parameters of registration and the deviation between the registered point cloud and its CAD model of the blade. Experimental results show that compared with state-of-the-art registration methods, the presented method gives higher priority to the parameters which are difficult to finish or repair by machining, and the position errors are controlled in the tolerance area, which effectively reduces the misjudgment. In addition, evaluation results of blade errors with the method are mainly reflected in profile, which is valuable for guiding the blade finishing or trimming in practice.

关键词： Structured light Point cloud registration Shape and position error coordinate descent algorithm Blade surface inspection

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SPARSE MODELING ON DISTRIBUTED ENCRYPTION DATA

SPARSE MODELING ON DISTRIBUTED ENCRYPTION DATA

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

作者： Bandoh, Yukihiro Nakachi, Takayuki Kiya, Hiroshi NTT Corp Yokosuka Kanagawa 2390847 Japan Tokyo Metropolitan Univ Tokyo 1910065 Japan

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Big-data analysis by edge/cloud systems is becoming more important. However, when information may lead to personal identification, such information tends to be encrypted and restricted to its owners to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful analysis. As a result, the desired analysis accuracy may not be achieved. To deal with this issue, several studies have examined encryptions based on the random unitary transform. This is because the random unitary transform has lower computational complexity than other encryption schemes, and its encryption domain supports several signal processing algorithms. However, analysis models on distributed encrypted data, have not been studied deeply enough. In this paper, we construct an analysis model for data encrypted with the random unitary transform by deriving a LASSO solution for encrypted data. The analytical model can derive the same LASSO solution as that yielded by processing the original data (i.e. without encryption). The analytical model supports distributed encryption, where a data set consists of different components that are encrypted at different sites independently. The collaboration enables us to improve the accuracy of analysis for distributed privacy-sensitive information.

关键词： LASSO coordinate descent algorithm random unitary transform

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Simultaneous estimation of quantile regression functions using B-splines and total variation penalty

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2019年 133卷 228-244页

作者： Jhong, Jae-Hwan Koo, Ja-Yong Korea Univ Dept Stat Seoul 02841 South Korea

We consider the problem of simultaneously estimating a finite number of quantile functions with B-splines and the total variation penalty. For the implementation of simultaneous quantile function estimators, we develop a new coordinate descent algorithm taking into account a special structure of the total variation penalty determined by B-spline coefficients. The entire paths of solution paths for several quantile function estimators and tuning parameters can be efficiently computed using the coordinate descent algorithm. We also consider non-crossing quantile function estimators having additional constraints at the knots of spline functions. Numerical studies using both simulated and real data sets are provided to illustrate the performance of the proposed method. For a theoretical result, we prove that the proposed the quantile regression function estimators achieve the minimax rate under regularity conditions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Binary method coordinate descent algorithm Minimax rate Non-crossing Total variation Weighted quantile

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High-Performance GLR Detector for Moving Target Detection in OFDM Radar-Based Vehicular Networks

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WIRELESS PERSONAL COMMUNICATIONS 2019年第2期108卷 751-768页

作者： Kafshgari, Shaghayegh Mohseni, Reza Samadi, Sadegh Khosravi, Mohammad Reza Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Nowadays, orthogonal frequency division multiplexing (OFDM) radars have been used in many applications such as target detection and recognition in vehicular networks and surveillance systems, according to their frequency diversity property. The model of the received signal in an OFDM radar based on target's parameters has a non-linear form with respect to unknown velocity and scattering coefficients, so there is no possibility of achieving a closed form solution for Maximum Likelihood Estimation of the unknown parameters and so the Neyman-Pearson detector. Therefore, in all published works, the generalized likelihood ratio (GLR) detector is obtained for the target with known velocity, or in case of simultaneous unknown velocity and scattering coefficients, only a wide two-dimensional grid search over all possible values of the unknown parameters is considered to maximize the Likelihood ratio. In this paper, a new method is proposed for simultaneous estimations of target velocity and scattering coefficients using a coordinate descent approach, which reduces the above nonlinear problem to two linear problems, and makes the implementation of GLR detector efficient. The simulation results confirm the efficiency of the proposed method.

关键词： OFDM radar Generalized likelihood ratio Maximum likelihood estimation coordinate descent algorithm

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Penalized empirical likelihood for the sparse Cox regression model

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2019年 201卷 71-85页

作者： Wang, Dongliang Wu, Tong Tong Zhao, Yichuan SUNY Upstate Med Univ Dept Publ Hlth & Prevent Med Syracuse NY 13210 USA Univ Rochester Dept Biostat & Computat Biol Rochester NY 14627 USA Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA

The current penalized regression methods for selecting predictor variables and estimating the associated regression coefficients in the sparse Cox model are mainly based on partial likelihood. In this paper, a bias-corrected empirical likelihood method is proposed for the sparse Cox model in conjunction with appropriate penalty functions when the dimensionality of data is high. Theoretical properties of the resulting estimator for the large sample are proved. Simulation studies suggest that penalized empirical likelihood works better than partial likelihood in terms of selecting correct predictors without introducing more model errors. The well-known primary biliary cirrhosis data set is used to illustrate the proposed penalized empirical likelihood method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： coordinate descent algorithm Bias-corrected empirical likelihood High-dimensional data Oracle property Penalized likelihood Sparse proportional hazards model

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Variable selection and estimation using a continuous approximation to the L₀ penalty

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2018年第1期70卷 191-214页

作者： Wang, Yanxin Fan, Qibin Zhu, Li Ningbo Univ Technol Sch Sci Ningbo 315211 Zhejiang Peoples R China Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Xiamen Univ Technol Sch Appl Math Xiamen 361024 Peoples R China

Variable selection problems are typically addressed under the regularization framework. In this paper, an exponential type penalty which very closely resembles the L-0 penalty is proposed, we called it EXP penalty. The EXP penalized least squares procedure is shown to consistently select the correct model and is asymptotically normal, provided the number of variables grows slower than the number of observations. EXP is efficiently implemented using a coordinate descent algorithm. Furthermore, we propose a modified BIC tuning parameter selection method for EXP and show that it consistently identifies the correct model, while allowing the number of variables to diverge. Simulation results and data example show that the EXP procedure performs very well in a variety of settings.

关键词： Penalized least squares coordinate descent algorithm Variable selection MBIC Oracle property

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Sparse factor regression via penalized maximum likelihood estimation

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STATISTICAL PAPERS 2018年第2期59卷 633-662页

作者： Hirose, Kei Imada, Miyuki Kyushu Univ Inst Math Ind Nishi Ku 744 Motooka Fukuoka 8190395 Japan NTT Network Innovat Labs 3-9-11 Midori Cho Musashino Tokyo 1808585 Japan

In factor regression model, the maximum likelihood estimation suffers from three disadvantages: (i) the maximum likelihood estimates are unavailable when the number of variables exceeds the number of observations, (ii) the rotation technique based on maximum likelihood estimates produces an insufficiently sparse loading matrix, and (iii) multicollinearity can occur when the estimates of unique variances (specific variances) are small because the regression coefficients are sensitive to the inverse of unique variances. To handle these problems, we propose a penalized maximum likelihood procedure. Specifically, we impose a lasso-type penalty on the factor loadings to improve the sparseness of the solution. We also introduce a penalty on unique variances, which (given the factor scores) corresponds to the ridge penalty on the regression coefficient. Theoretical properties from a prediction viewpoint of our procedure are discussed. The effectiveness of the procedure is investigated through Monte Carlo simulations. The utility of our procedure is demonstrated on real data collected by an online questionnaire.

关键词： coordinate descent algorithm Lasso Multicollinearity Penalized maximum likelihood estimation Rotation technique

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