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IEEE Radar Conference (RadarConf)

作者： Rosamilia, Massimo Lan, Lan Aubry, Augusto De Maio, Antonio Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781728153681

The problem of adaptive radar detection with a polarimetric Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar is addressed in this paper. The target detection problem, formulated as a composite hypothesis test, is tackled resorting to sub-optimal design strategies based on the Generalized Likelihood Ratio (GLR) criterion. The resulting detectors demand the solution of a box-constrained optimization problem, for which two iterative techniques, leveraging the Gradient Projection Method (GPM) and the coordinate descent (CD) algorithm, respectively, are devised. At the analysis stage, the performance of the proposed architectures is evaluated via Monte Carlo simulations and compared with benchmark detectors in both white and colored disturbance.

关键词： polarimetric FDA-MIMO radar target detection coordinate descent algorithm gradient projection method

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A hybrid algorithm for the search of long binary sequences with low aperiodic autocorrelations

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SOFT COMPUTING 2021年第20期25卷 12725-12744页

作者： Zhang, Mingxing Zhou, Zhengchun Yang, Meng Liu, Zilong Yang, Yang Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China Southwest Jiaotong Univ Sch Math Chengdu Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

Binary sequences with low aperiodic autocorrelation play an important role in communication and radar. The search of long low autocorrelation binary sequences (LABS) is a classical but challenging research problem. Exhaustive sequence search is infeasible for large lengths due to its prohibitively high complexity. Albeit many algorithms on LABS search in the literature, they are effective for lengths up to a few hundred only. In this paper, we propose an efficient hybrid algorithm which combines the strength of coordinate descent algorithm and the simulated annealing algorithm. Our proposed hybrid algorithm can efficiently generate long LABS of lengths up to several thousand and outperforms existing approaches in most cases.

关键词： Aperiodic autocorrelation Binary sequence Hybrid algorithm coordinate descent algorithm Simulated annealing algorithm

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Penalized I-spline monotone regression estimation

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2021年第11期50卷 3714-3732页

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

We propose a penalized regression spline estimator for monotone regression. To construct the estimator, we adopt the I-splines with the total variation penalty. The I-splines lend themselves to the monotonicity because of the simpler form of restrictions, and the total variation penalty induces a data-driven knot selection scheme. A coordinate descent algorithm is developed for the estimator. If the number of complexity parameter candidates sufficiently increases, the algorithm considers all possible monotone linear spline fits to the given data. The pruning process of the algorithm not only provides numerical stability, but also implements the data-driven knot selection. We also compute the maximum candidate of the complexity parameter to facilitate complexity parameter selection. Extensive numerical studies show that the proposed estimator captures spatially inhomogeneous behaviors of data, such as sudden jumps.

关键词： coordinate descent algorithm I-splines Knot selection Maximum complexity parameter Monotone regression Total variation penalty

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coordinate optimization for generalized fused Lasso

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2021年第24期50卷 5955-5973页

作者： Ohishi, M. Fukui, K. Okamura, K. Itoh, Y. Yanagihara, H. Hiroshima Univ Grad Sch Sci Higashihiroshima Japan Osaka Med Coll Res & Dev Ctr Takatsuki Osaka Japan Tokyo Kantei Co Ltd Shinagawa Japan Hiroshima Univ Educ & Res Ctr Artificial Intelligence & Data Inn Hiroshima Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan

Fused Lasso is one of extensions of Lasso to shrink differences of parameters. We focus on a general form of it called generalized fused Lasso (GFL). The optimization problem for GFL can be came down to that for generalized Lasso and can be solved via a path algorithm for generalized Lasso. Moreover, the path algorithm is implemented via the genlasso package in R. However, the genlasso package has some computational problems. Then, we apply a coordinate descent algorithm (CDA) to solve the optimization problem for GFL. We give update equations of the CDA in closed forms, without considering the Karush-Kuhn-Tucker conditions. Furthermore, we show an application of the CDA to a real data analysis.

关键词： coordinate descent algorithm generalized fused Lasso generalized Lasso

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Generalized fused Lasso for grouped data in generalized linear models

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STATISTICS AND COMPUTING 2024年第4期34卷 124-124页

作者： Ohishi, Mineaki Tohoku Univ Ctr Data Driven Sci & Artificial Intelligence Kawauchi 41Aoba Ku Sendai Miyagi 9808576 Japan

Generalized fused Lasso (GFL) is a powerful method based on adjacent relationships or the network structure of data. It is used in a number of research areas, including clustering, discrete smoothing, and spatio-temporal analysis. When applying GFL, the specific optimization method used is an important issue. In generalized linear models, efficient algorithms based on the coordinate descent method have been developed for trend filtering under the binomial and Poisson distributions. However, to apply GFL to other distributions, such as the negative binomial distribution, which is used to deal with overdispersion in the Poisson distribution, or the gamma and inverse Gaussian distributions, which are used for positive continuous data, an algorithm for each individual distribution must be developed. To unify GFL for distributions in the exponential family, this paper proposes a coordinate descent algorithm for generalized linear models. To illustrate the method, a real data example of spatio-temporal analysis is provided.

关键词： Grouped data coordinate descent algorithm Generalized fused Lasso Generalized linear models Multivariate trend filtering

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Usage of the GO estimator in high dimensional linear models

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COMPUTATIONAL STATISTICS 2021年第1期36卷 217-239页

作者： Genc, Murat Ozkale, M. Revan Cukurova Univ Fac Sci & Letters Dept Stat TR-01330 Adana Turkey

This paper discusses simultaneous parameter estimation and variable selection and presents a new penalized regression method. The method is based on the idea that the coefficient estimates are shrunken towards a predetermined coefficient vector which represents the prior information. This method can result in smaller length estimates of the coefficients depending on the prior information compared to elastic net. In addition to the establishment of the grouping property, we also show that the new method has the grouping effect when the predictors are highly correlated. Simulation studies and real data example show that the prediction performance of the new method is improved over the well-known ridge, lasso and elastic net regression methods yielding a lower mean squared error and competes about the variable selection under sparse and non-sparse situations.

关键词： coordinate descent algorithm Elastic net Grouping property Lasso Shrinkage Variable selection

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Outlier detection under a covariate-adjusted exponential regression model with censored data

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COMPUTATIONAL STATISTICS 2021年第2期36卷 961-976页

作者： Pan, Yingli Liu, Zhan Song, Guangyu Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Peoples R China

Exponential regression models with censored data are most widely used in practice. In the modeling process, there exist situations where the covariates are not directly observed but are observed after being contaminated by unknown functions of an observable confounder in a multiplicative manner. The problem of outlier detection is a fundamental and important problem in applied statistics. In this paper, we use a nonparametric regression method to adjust the covariates and recast the outlier detection issue into a high-dimensional regularization regression issue in the covariate-adjusted exponential regression model with censored data. We propose a smoothly clipped absolute deviation (SCAD) penalized likelihood method to detect the possible outliers, which features that the proposed method can simultaneously deal with outlier detection and estimations for the regression coefficients. The coordinate descent algorithm is employed to facilitate computation. Simulation studies are conducted to evaluate the finite-sample performance of our proposed method. An application to a German breast cancer study demonstrates the utility of the proposed method in practice.

关键词： Outlier diagnosis Contaminated covariate SCAD coordinate descent algorithm

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Additive Poisson regression via forced categorical covariates and generalized fused Lasso

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Procedia Computer Science 2023年 225卷 1987-1996页

作者： Mariko Yamamura Mineaki Ohishi Hirokazu Yanagihara

In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates.

关键词： Additive Poisson regression Spatio-temporal analysis Generalized fused Lasso coordinate descent algorithm

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A coordinate descent MM algorithm for fast computation of sparse logistic PCA

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2013年 62卷 26-38页

作者： Lee, Seokho Huang, Jianhua Z. Hankuk Univ Foreign Studies Dept Stat Seoul South Korea Texas A&M Univ Dept Stat College Stn TX 77843 USA

Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Binary data coordinate descent algorithm MM algorithm Penalized maximum likelihood Principal component analysis

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Spatio-Temporal Adaptive Fused Lasso for Proportion Data 13th

Spatio-Temporal Adaptive Fused Lasso for Proportion Data

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

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Radiat Effects Res Fdn Dept Stat Minami Ku 5-2 Hijiyama Pk Hiroshima 7320815 Japan Hiroshima Univ Educ & Res Ctr AI & Data Innovat Naka Ku 1-1-89 Higashi Senda Hiroshima 7300053 Japan Hiroshima Univ Grad Sch Adv Sci & Engn 1-3-1 Kagamiyama Higashihiroshima Hiroshima 7398526 Japan

ISBN: (纸本)9789811627651;9789811627644

Population-corrected rates are often used in statistical documents that show the features of a municipality. In addition, it is important to determine changes of the features over time, and for this purpose, data collection is continually carried out by census. In the present study, we propose a method for analyzing the spatiotemporal effects on rates by adaptive fused lasso. For estimation, the coordinate descent algorithm, which is known to have better estimation accuracy and speed than the algorithm used in genlasso in the R software package, is used for optimization. Based on the results of the real data analysis for the crime rates in the Kinki region of Japan in 1995-2008, the proposed method can be applied to spatio-temporal proportion data analysis.

关键词： Spatio-temporal proportion data Adaptive fused lasso coordinate descent algorithm

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