Algorithm 1.

Pseudocode of the proposed feature selection algorithm.

	Input : Data $𝒟={\left\{\left({\mathbf{x}}_n,y_n\right)\right\}}_{n=1}^N\subset {ℝ}^J\times \left\{\pm 1\right\}$,     kernel width σ, regularization parameter     λ, stop criterion θ
	Output: Feature weights w
1	Initialization: Set w(0) = 1, t = 1 ;
2	repeat
3	Compute d(xn, xi|w(t−1), ∀xn, xi ∈ 𝒟
4	Compute P(xi=NM(xn)|w(t−1)) and P(xj=NH(xn)|w(t−1)) as in (4) and (5);
5	Solve for v through gradient descent using the update rule specified in (10);
6	$w_j^{(t)}=v_j^2,1\le j\le j;$
7	t = t +1 ;
8	until   ‖w(t) − w(t−1)‖ < θ;
9	w = w(t).