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Sparse PCA with Oracle Rate in High Dimensions | |
2025-04-11 | |
会议录名称 | ICASSP 2025 - 2025 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
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ISSN | 1520-6149 |
发表状态 | 已发表 |
DOI | 10.1109/ICASSP49660.2025.10888203 |
摘要 | In this paper, we study the sparse principal component analysis (PCA) problem in high-dimensional settings. We propose a novel row-sparse principal subspace estimator, which estimates the subspace spanned by multiple eigenvectors based on the variance maximization problem with a nonconvex sparsity regularizer. To tackle the nonconvex estimation problem, we introduce a minorization-maximization (MM) algorithm to decompose it into a sequence of convex subproblems. Each subproblem is solved based on the alternating direction method of multipliers. Theoretically, we provide a comprehensive analysis of both the computational and statistical properties of the iterates from the MM algorithm. We demonstrate that the proposed sparse PCA estimator can achieve the same statistical rate as the oracle estimator. Simulation results corroborate the theoretical findings and highlight the superiority of the proposed sparse PCA estimator. |
会议录编者/会议主办者 | IEEE ; IEEE Signal Processing Society |
会议名称 | 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 |
会议地点 | Hyderabad, India |
会议日期 | 6-11 April 2025 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20251818340883 |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/496899 |
专题 | 信息科学与技术学院 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_赵子平组 |
作者单位 | School of Information Science and Technology, ShanghaiTech University, Shanghai, China |
第一作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Wenfu Zhong,Ziping Zhao. Sparse PCA with Oracle Rate in High Dimensions[C]//IEEE, IEEE Signal Processing Society:Institute of Electrical and Electronics Engineers Inc.,2025. |
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