  In this paper, we presented a novel semi-supervised one-class classification
algorithm which assumes that class is linearly separable from other elements.
We proved theoretically that class is linearly separable if and only if it is
maximal by probability within the sets with the same mean. Furthermore, we
presented an algorithm for identifying such linearly separable class utilizing
linear programming. We described three application cases including an
assumption of linear separability, Gaussian distribution, and the case of
linear separability in transformed space of kernel functions. Finally, we
demonstrated the work of the proposed algorithm on the USPS dataset and
analyzed the relationship of the performance of the algorithm and the size of
the initially labeled sample.
