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Robust enhanced indexation optimization with sparse industry layout constraint

COMPUTERS & OPERATIONS RESEARCH 2024年 161卷

作者： Allen-Zhao, Zhihua Xu, Fengmin Dai, Yuhong Liu, Sanyang Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Xi An Jiao Tong Univ Sch Econ & Finance Xian 710061 Peoples R China Chinese Acad Sci Acad Math & Syst Sci LSEC ICMSEC Beijing 100190 Peoples R China

In this paper, we investigate an enhanced indexation methodology using robust Conditional Value-at-Risk (CVaR) and group-sparse optimization. A featured difference from the existing literatures is to describe the tail risk using the worst-case CVaR of excess returns, and the process of industry selection using a weighted l(infinity,1) -norm constraint. We develop an accelerated alternating minimization algorithm for solving this problem. At each iteration, this method usually alternately solves a convex cone program, which admits a closed-form solution via convex duality theory, and a projection problem onto a weighted infinity-to-one-ball, where a fixed-point iteration projection method is developed, terminating in finite number of iterations. The global convergence rates in terms of the primal and dual residuals are also provided. Empirical tests on actual data sets are presented to demonstrate the superior out-of-sample performance of our proposed strategy

关键词： Group-sparse optimization alternating minimization algorithm Worst-case CVaR Enhanced indexation Out-of-sample analysis

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Nonlocal Structured Sparsity Regularization Modeling for Hyperspectral Image Denoising

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Zha, Zhiyuan Wen, Bihan Yuan, Xin Zhang, Jiachao Zhou, Jiantao Lu, Yilong Zhu, Ce Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Westlake Univ Sch Engn Hangzhou 310024 Zhejiang Peoples R China Nanjing Inst Technol Artificial Intelligence Inst Ind Technol Nanjing 211167 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

The nonlocal-based model for hyperspectral image (HSI) denoising first uses nonlocal self-similarity (NSS) prior to group similar full-band patches into 3-D nonlocal full-band groups (tensors) using a block matching (BM) operation, and then a low-rank (LR) penalty is typically applied to each nonlocal full-band group to reduce noise. While nonlocal-based methods have shown promising performance in HSI denoising, most existing methods have only considered the LR property of the nonlocal full-band group while ignoring the strong correlation between sparse coefficients. Moreover, such methods often result in unsatisfactory visual artifacts due to the noise sensitivity of BM operations, while requiring expensive computations. To address these limitations, this article proposes a novel nonlocal structured sparsity regularization (NLSSR) approach for HSI denoising. First, to mitigate the noise sensitivity of the BM operation, we propose a graph-based domain distance scheme to index similar full-band patches to form the nonlocal full-band group. Second, we design an adaptive unidirectional LR dictionary with low complexity that takes into account the differences in intrinsic structure correlation among different modes of the nonlocal full-band tensor. Third, we utilize a global spectral LR prior to reduce spectral redundancy. Fourth, we develop a generalized soft-thresholding (GST) algorithm based on the alternating minimization framework to solve the NLSSR-based HSI denoising problem. We perform extensive experiments on both simulated and real data to show that the proposed NLSSR algorithm outperforms many popular or state-of-the-art HSI denoising methods in both quantitative and visual evaluations.

关键词： Noise reduction Tensors Noise measurement Visualization Image denoising Correlation Sensitivity alternating minimization algorithm generalized soft-thresholding (GST) graph-based block matching (BM) Index Terms hyperspectral image (HSI) denoising low-rank (LR) nonlocal self-similarity (NSS) structured sparsity

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Fault Detection Based on Canonical Correlation Analysis with Rank Constrained Optimization 40

Fault Detection Based on Canonical Correlation Analysis with...

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40th Chinese Control Conference (CCC)

作者： Zhang, Yuhan Xiu, Xianchao Yang, Ying Liu, Wanquan Peking Univ Dept Mech & Engn Sci Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

ISBN: (纸本)9789881563804

Canonical correlation analysis (CCA) has attracted increasing attention in the field of fault detection because it provides an effective way to explore the relationship between the input and output data. This paper develops a novel rank constrained CCA (RCCCA) framework, which is the first approach that takes the rank prior information into consideration. Technically, the rank constrained optimization is able to capture the global structures of variables, and thus improve the performance of fault detection. In order to solve RCCCA, an alternating minimization algorithm is designed, which aims to preserve the maximum correlation with the low-rank learning. A fault detection residual is then generated, and the test statistic is constructed to determine whether a fault occurs. The RCCCA-based fault detection is finally tested on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency and feasibility of the proposed method.

关键词： Fault detection canonical correlation analysis rank constrained optimization alternating minimization algorithm Tennessee Eastman process

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Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty

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REMOTE SENSING 2022年第18期14卷 4463-4463页

作者： Wang, Degen Wang, Tong Cui, Weichen Liu, Cheng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, a new optimization objective function based on a subspace penalty is established. Second, the closed-form solution of each minimization step is obtained through the alternating minimization algorithm, which can guarantee the convergence of the algorithm. Finally, a restart strategy is used to adaptively update the support, which reduces the computational complexity. Simulation results show that the proposed algorithm has excellent performance in clutter suppression, convergence speed and running time with insufficient training samples.

关键词： space-time adaptive processing subspace penalty alternating minimization algorithm restart strategy

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Direct Position Determination of Multiple Constant Modulus Sources Based on Direction of Arrival and Doppler Frequency Shift

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第1期39卷 268-306页

作者： Wang, Ding Yin, Jie-xin Chen, Xin Jia, Chang-gui Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450002 Peoples R China Zhengzhou Inst Informat Sci & Technol Zhengzhou 450002 Henan Peoples R China

Direct position determination (DPD) methods are known to outperform classical two-step localization methods under the condition of low signal-to-noise ratios and/or small number of snapshots. Additionally, they can directly exploit the prior knowledge of signal waveforms to achieve higher estimation accuracy. In this paper, we concentrate on the DPD method for locating multiple constant modulus (CM) sources based on a single moving receiving station. Both direction of arrival and Doppler frequency shift are used for source localization. First, a received array signal model with a small time delay that can incorporate the Doppler frequency information is formed. Subsequently, the corresponding maximum likelihood estimation criterion is established. An effective alternating minimization algorithm is developed to solve the optimization problem. Subsequently, we also derive the Cramer-Rao bound expressions for parameter estimation. It is proved that the CM property of the signals is useful in reducing the lower bound for location estimation. Simulation results demonstrate that the proposed method is asymptotically efficient. Moreover, it is also shown that the estimation accuracy of direct localization can be greatly increased if the CM characteristics of the radiated signals are adequately exploited.

关键词： Source localization DPD DOA Doppler frequency Constant modulus alternating minimization algorithm CRB

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Simultaneous Image Enhancement and Restoration with Non-convex Total Variation

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JOURNAL OF SCIENTIFIC COMPUTING 2021年第3期87卷 83-83页

作者： Kang, Myeongmin Jung, Miyoun Chungnam Natl Univ Dept Math Daejeon South Korea Hankuk Univ Foreign Studies Dept Math Yongin South Korea

In this article, we propose a novel variational model for the joint enhancement and restoration of low-light images corrupted by blurring and/or noise. The model decomposes a given low-light image into reflectance and illumination images that are recovered from blurring and/or noise. In addition, our approach utilizes non-convex total variation regularization on all variables. This allows us to adequately denoise homogeneous regions while preserving the details and edges in both reflectance and illumination images, which leads to clean and sharp final enhanced images. To solve the non-convex model, we employ a proximal alternating minimization approach, and then an iteratively reweighted l1 algorithm and an alternating direction method of multipliers are adopted for solving the subproblems. These techniques contribute to an efficient iterative algorithm, with its convergence proven. Experimental results demonstrate the effectiveness of the proposed model when compared to other state-of-the-art methods in terms of both visual aspect and image quality measures.

关键词： Retinex Image enhancement Image restoration Non-convex total variation alternating minimization algorithm Iteratively reweighted l1 algorithm 68U10 65K10 94A08

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Fault Detection Based on Canonical Correlation Analysis with Rank Constrained Optimization

Fault Detection Based on Canonical Correlation Analysis with...

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第40届中国控制会议

作者： Yuhan Zhang Xianchao Xiu Ying Yang Wanquan Liu State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of EngineeringPeking University School of Intelligent Systems Engineering Sun Yat-sen University

Canonical correlation analysis（CCA） has attracted increasing attention in the field of fault detection because it provides an effective way to explore the relationship between the input and output *** paper develops a novel rank constrained CCA（RCCCA） framework,which is the first approach that takes the rank prior information into ***,the rank constrained optimization is able to capture the global structures of variables,and thus improve the performance of fault *** order to solve RCCCA,an alternating minimization algorithm is designed,which aims to preserve the maximum correlation with the low-rank learning.A fault detection residual is then generated,and the test statistic is constructed to determine whether a fault *** RCCCA-based fault detection is finally tested on a numerical example and the Tennessee Eastman benchmark *** results indicate the efficiency and feasibility of the proposed method.

关键词： Fault detection canonical correlation analysis rank constrained optimization alternating minimization algorithm Tennessee Eastman process

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Blind separation of structural modes by compact-bandwidth regularization

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 131卷 288-316页

作者： Wang, Li Huang, Min Lu, Zhong-Rong Sun Yat Sen Univ Sch Aeronaut & Astronaut Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Guangdong Peoples R China

Operational modal analysis (OMA) has received tremendous interest from engineering fields in recent years. This paper develops a compact-bandwidth regularization approach for OMA within the signal processing framework. The key ingredient lies in the fact that a structural mode is always compact in the frequency domain and this results in the compact-bandwidth constraint for structural modes. To implicitly enforce the compact-bandwidth constraint, the compact-bandwidth regularization is introduced to conventional blind modal separation. Then, the alternating minimization algorithm is used to iteratively get the solution in the immediately-at-once or one-by-one manner. In proceeding so, the spectrum-peak-based rule is adopted for choice of initial modal parameters and the L-curve method in conjunction with a theoretically derived bound is applied to estimate a proper regularization parameter. Numerical examples and an experimental test case are studied along with comparison to some usual OMA approaches to see the performance and advantages of the proposed approach in modal identification. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Compact-bandwidth regularization Operational modal analysis alternating minimization algorithm Regularization parameter estimation Closely spaced modes

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A Penalized Likelihood Method for Classification With Matrix-Valued Predictors

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2019年第1期28卷 11-22页

作者： Molstad, Aaron J. Rothman, Adam J. Fred Hutchinson Canc Res Ctr Biostat Program Seattle WA 98109 USA Univ Minnesota Sch Stat Minneapolis MN 55455 USA

We propose a penalized likelihood method to fit the linear discriminant analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties encourage pairs of response category mean matrix estimators to have equal entries and also encourage zeros in the precision matrix estimator. To compute our estimators, we use a blockwise coordinate descent algorithm. To update the optimization variables corresponding to response category mean matrices, we use an alternating minimization algorithm that takes advantage of the Kronecker structure of the precision matrix. We show that our method can outperform relevant competitors in classification, even when our modeling assumptions are violated. We analyze three real datasets to demonstrate our method's applicability. Supplementary materials, including an R package implementing our method, are available online.

关键词： alternating minimization algorithm Classification Penalized likelihood

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Robust direct position determination methods in the presence of array model errors

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1页

作者： Wang, Ding Yin, Jiexin Liu, Ruirui Wu, Zhidong Tang, Tao Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450002 Henan Peoples R China Zhengzhou Informat Sci & Technol Inst Zhengzhou 450002 Henan Peoples R China

Direct position determination (DPD) methods are known to have many advantages over the traditional two-step localization method, especially for low signal-to-noise ratios (SNR) and/or short data records. However, similar to conventional direction-of-arrival (DOA) estimation methods, the performance of DPD estimators can be dramatically degraded by inaccuracies in the array model. In this paper, we present robust DPD methods that can mitigate the effects of these uncertainties in the array manifold. The proposed technique is related to the classical auto-calibration procedure under the assumption that prior knowledge of the array response errors is available. Localization is considered for the cases of both unknown and a priori known transmitted signals. The corresponding maximum a posteriori (MAP) estimators for these two cases are formulated, and two alternating minimization algorithms are derived to determine the source location directly from the observed signals. The Cramer-Rao bounds (CRBs) for position estimation are derived for both unknown and known signal waveforms. Simulation results demonstrate that the proposed algorithms are asymptotically efficient and very robust to array model errors.

关键词： Array signal processing Source localization Direct position determination (DPD) Array model errors alternating minimization algorithm Cramer-Rao bound (CRB)

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