While the quality of object recognition systems can strongly benefit from more data, human annotation and labeling can hardly keep pace. This motivates the usage of autonomous and unsupervised learning methods. In this paper, we present a simple, yet effective method for unsupervised image categorization, which relies on discriminative learners. Since automatically obtaining error-free labeled training data for the learners is infeasible, we propose the concept of weak training (WT) set. WT sets have various deficiencies, but still carry useful information. Training on a single WT set cannot result in good performance, thus we design a random walk sampling scheme to create a series of diverse WT sets. This naturally allows our categorization learning to leverage ensemble learning techniques. In particular, for each WT set, we train a max-margin classifier to further partition the whole dataset to be categorized. By doing so, each WT set leads to a base partitioning of the dataset and all the base partitionings are combined into an ensemble proximity matrix. The final categorization is completed by feeding this proximity matrix into a spectral clustering algorithm. Experiments on a variety of challenging datasets show that our method outperforms competing methods by a considerable margin.