One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean

TL;DR

This paper introduces a semi-supervised one-class classification method based on the mean, with theoretical proofs of linear separability and practical algorithms tested on USPS data, exploring effects of labeled sample size.

Contribution

It presents a novel semi-supervised algorithm for one-class classification assuming linear separability, with theoretical proof and multiple application scenarios.

Findings

Algorithm works on USPS dataset

Performance depends on initial labeled sample size

Applicable to linear, Gaussian, and kernel-transformed spaces

Abstract

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.