Semi-supervised handwritten digit recognition using very few labeled data

We propose a novel semi-supervised classifier for handwritten digit recognition problems that is based on the assumption that any digit can be obtained as a slight transformation of another sufficiently close digit. Given a number of labeled and unlabeled images, it is possible to determine the class membership of each unlabeled image by creating a sequence of such image transformations that connect it, through other unlabeled images, to a labeled image. In order to measure the total transformation, a robust and reliable metric of the path length is proposed, which combines a local dissimilarity between consecutive images along the path with a global connectivity-based metric. For the local dissimilarity we use a symmetrized version of the zero-order image deformation model (IDM) proposed by Keysers et al. in [1]. For the global distance we use a connectivity-based metric proposed by Chapelle and Zien in [2]. Experimental results on the MNIST benchmark indicate that the proposed classifier outperforms current state-of-the-art techniques, especially when very few labeled patterns are available.