Multi-view learning is a task of learning from multiple sources with co-occurred samples. Here, I will talk about multi-view learning techniques which find shared information between multiple sources in an unsupervised setting. We use statistical dependence as a measure to find shared information. Multi-view learning becomes more challenging and interesting (i) without co-occurred samples in multiple views and (ii) with arbitrary collection of matrices. I will present our work around these two problems with the help of some practical applications.

Dr. Abhishek Tripathi is working as a Research Scientist in Xerox Research Centre India (XRCI), Bangalore since January 2012. He is part of the Machine Learning group, where the focus domains include Transportation, Healthcare and Human Resource. Prior to XRCI, Abhishek had spent one year at Xerox Research Centre Europe, France. He received his PhD in Computer Science from University of Helsinki, Finland. His research interests include unsupervised multi-view learning, matrix factorization, recommender systems, data fusion and dimensionality reduction.