Classification of multiple observations by semi-supervised learning

We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown class. We propose a modified Transductive Support Vector Machine algorithm, which captures the specific nature of the classification problem. We further propose a lower complexity solution that is able to exploit the properties of the data manifolds with a graph-based algorithm. Hence, we formulate the computation of the unknown label matrix as a smoothing process on the manifold under the constraint that all observations represent an object of one single class. It results into a discrete optimization problem, which can be solved by an efficient and low complexity algorithm. We demonstrate the performance of the proposed algorithms in the classification of sets of multiple images. In particular, we show the high potential of the novel graph-based solution in videobased face recognition, where it outperforms state-of-the-art solutions that fall short of exploiting the manifold structure of the face image data sets.