Sparse Modeling of Graph-Structured Data ... and ... Images
March 13 - 15, 2014
The Institute of Statistical Mathematics, Tachikawa, Tokyo

Images, video, audio, text documents, financial data, medical information, traffic info – all these and many others are data sources that can be effectively processed. Why? Is it obvious? In this talk we will start by discussing “modeling” of data as a way to enable their actual processing, putting emphasis on sparsity-based models. We will turn our attention to graph-structured data and propose a tailored sparsifying transform for its dimensionality reduction and subsequent processing. We shall conclude by showing how this new transform becomes relevant and powerful in revisiting … classical image processing tasks..

This is a joint work with Idan Ram and Israel Cohen. This talk was given as a plenary talk in a Workshop on Mathematical Approaches to Large-Dimensional Data Analysis