Compute the expectation:
    <K_{X, Z}>_p(X)
        - K_{.,.} :: Linear kernel

    :return: NxM

def _expectation(p, kern, feat, none1, none2, nghp=None):
    """
    Compute the expectation:
    <K_{X, Z}>_p(X)
        - K_{.,.} :: Linear kernel

    :return: NxM
    """
    with params_as_tensors_for(kern, feat):
        # use only active dimensions
        Z, Xmu = kern._slice(feat.Z, p.mu)

        return tf.matmul(Xmu, Z * kern.variance, transpose_b=True)