Recent years have witnessed new frameworks/algorithms able to deal with multiple views, such as Multiple Kernel Learning (MKL), Boosting, Co-regularized approach. Such algorithms come from the Machine Learning community and find applications in many different areas, such as Multimedia Indexing, Computer Vision, Bio-informatics, Neuro-imaging... Multiview learning, naturally enough, emphasise the potential benefits of learning through collaboration with multiple sources of data (e.g. video document can be described through images, sound, motion, text). Depending on the context, this issue of learning from multiple descriptions of data goes under the name of multiview learning (machine learning, computer vision), multimodality fusion (multimedia), among others. This workshop is the opportunity to bring together theoretical and applicative communities around multiview learning, which could lead to significant contributions in Machine Learning, Multimedia Mining and Computer Vision. This workshop builds upon successful previous machine learning workshops on multiview learning or connections between ML and applications, like Machine Learning techniques for processing multimedia content (ICML 2005), Learning with multiples views (ICML 2005), Learning from multiples sources (NIPS 2008), Learning from multiples sources with applications to robotics (NIPS 2009) where links between theory and applications of the multiview paradigms are made. The literature and the advances on multiview learning have grown up to a point where a broad synthesis is required. The workshop will be held at Nancy, France, on september 19th.