. Author manuscript; available in PMC: 2017 Jun 12.

Published in final edited form as: Adv Neural Inf Process Syst. 2015;28:2512–2520.

Algorithm 4.

High Dimensional EM Algorithm with Resampling.

1:	Parameter: Sparsity Parameter ŝ, Maximum Number of Iterations T
2:	Initialization: 𝒮̂^init ← supp(β^init, ŝ), β⁽⁰⁾ ← trunc(β^init, 𝒮̂^init),
	{supp(·, ·) and trunc(·, ·) are defined in (2.6) and (2.7)}
	Split the Dataset into T Subsets of Size n/T
	{Without loss of generality, we assume n/T is an integer}
3:	For t = 0 to T − 1
4:	E-step: Evaluate Q_n/T (β; β^(t)) with the t-th Data Subset
5:	M-step: β^(t+0.5) ← M_n/T(β^(t))
	{M_n/T(·) is implemented as in Algorithm 2 or 3 with Q_n(·; ·) replaced by Q_n/T(·; ·)}
6:	T-step: 𝒮̂^(t+0.5) ← supp(β^(t+0.5), ŝ), β^(t+1) ← trunc(β^(t+0.5), 𝒮̂^(t+0.5))
7:	End For
8:	Output: β̂ ← β^(T)