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where <math>\psi_m(x)=[K_m(x_1,x),\ldots,K_m(x_L,x)]^T</math> (the kernel distance between the labeled data and all of the labeled and unlabeled data) and <math>\phi^{\pi}_m</math> is a non-negative random vector with a 2-norm of 1. The value of <math>\Pi</math> is the number of times each kernel is projected. Expectation regularization is then performed on the MKD, resulting in a reference expectation <math>q^{pi}_m(y|g^{\pi}_m(x))</math> and model expectation <math>p^{\pi}_m(f(x)|g^{\pi}_m(x))</math>. Then, we define
:<math>\Theta=\frac{1}{\Pi} \
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