Abstract In this paper, we propose an online alternating minimization (OAM) algorithm to estimate the sparse coefficients of stochastic regression models from time-series data. We apply the alternating minimization (AM) directly to the penalty function of the variant of the least absolute shrinkage and selection operator (Lasso), which leads to convex subproblems, and thereby can be solved efficiently. Moreover, under certain mild assumptions, we derive a convergence analysis framework and establish the strong consistency for the OAM estimator. Numerical experiments demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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回归模型的在线稀疏识别

摘要 在本文中，我们提出了一种在线交替最小化 (OAM) 算法，用于从时间序列数据估计随机回归模型的稀疏系数。我们将交替最小化（AM）直接应用于最小绝对收缩和选择算子（Lasso）的变体的惩罚函数，这导致凸子问题，从而可以有效地解决。此外，在某些温和的假设下，我们导出了一个收敛分析框架，并为 OAM 估计量建立了强一致性。数值实验证明了所提出算法的有效性。