Understanding urban mobility is a fundamental question for institutional organizations (transport authorities, city halls) and it involves many different fields like social sciences, urbanism or geography. With the increasing number of probes tracking human locations, like magnetic pass for urban transportation, road sensors, CCTV systems or cell phones, mobility data are exponentially growing. Mining the activity logs in order to model and characterize efficiently our mobility patterns is a challenging task involving large scale noisy datasets. In this article, we present a robust approach to characterize activity patterns from the activity logs of a urban transportation network. Our study focuses on the Paris subway network. Our dataset includes more than 80 millions travels by 600k users. The proposed approach is based on a multi-scale representation of the user activities, coupled to a nonnegative matrix factorization algorithm. The latter is used to learn dictionaries of usages that can be exploited in order to characterize user mobility and station visits patterns. The relevance of the extracted dictionaries is then assessed by using them to cluster users and stations. This analysis shows that public transportation usage patterns are tightly linked to sociological patterns.