LIVES in few words

Learning with interacting views ?

Imagine you are asked to answer the following questions. How can we design a computer-aided diagnosis tool for neurological disorders from multiple brain images acquired with different medical imaging devices? How can a computer identify the emotion felt by a person from her face, voice, heart beat, etc.? How can such tools remain efficient when the data quality is poor or with missing data? In practice, the following problem comes up: building a classifier capable of predicting the class (the diagnosis or the emotion) of a given object by taking advantage of the multiple modalities or views used to depict the objects. This is precisely what the present project aims at: the development of a well-founded machine learning framework for learning in the presence of multiple and interacting views and its confrontation to real-world problems.

This project aims at addressing these questions by gathering five partners. Ironova, a startup company which promotes interactive gaming through sensor devices, and the Institut des Neurosciences de la Timone (INT, Marseille) actually face these questions in the heart of their activities. Three complementary machine learning teams from the Laboratoire d’Informatique de Paris 6 (LIP6), the Laboratoire Hubert Curien (LaHC, Saint Etienne) and the Laboratoire d’Informatique Fondamentale de Marseille (LIF, which heads the consortium), form a fundamental research network that will push the limits of the methodological state of the art. LIVES aims at filling a hole in the current machine learning state of the art, by providing new theoretical work based on the characterization of the interactions between views, and enabling the construction of new methods and algorithms for multiview learning.

This work will be carried on by a strong core of three machine learning teams, and will be confronted with real datasets provided by the two other partners. Altogether, this multidisciplinary consortium will benefit from cross-domain interactions between fundamental computer science, brain imaging, and affective computing specialists, providing new understandings for their common problems and solutions.

Cross-view Learning: theory, algorithms and benchmarks