稳健主成分分析
离群值
计算机科学
算法
稳健统计
主成分分析
估计员
基质(化学分析)
功能(生物学)
稳健性(进化)
稀疏矩阵
合成数据
数学优化
人工智能
数学
生物化学
统计
材料科学
化学
物理
量子力学
进化生物学
高斯分布
基因
复合材料
生物
作者
Zhi-Yong Wang,Hing Cheung So,Abdelhak M. Zoubir
标识
DOI:10.1109/tsp.2023.3290353
摘要
As a widely-used tool to resist outliers, the correntropy criterion or Welsch function has recently been exploited for robust matrix recovery. However, it down-weighs all observations including uncontaminated data. On the other hand, its implicit regularizer (IR) cannot achieve sparseness, which is a desirable property in many practical scenarios. To address these two issues, we devise a novel M-estimator called hybrid ordinary-Welsch (HOW) function, which only down-weighs the outlier-contaminated data, and the IR generated by the HOW can attain sparseness. To verify the effectiveness of the HOW function, we apply it to robust matrix completion and principal component analysis. An efficient algorithm is developed and we prove that any generated limit point is a critical point. Finally, extensive experimental results based on synthetic and real-world data demonstrate that the proposed approach outperforms the state-of-the-art methods in terms of recovery accuracy and runtime.
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