@misc{elad_sparse_2014,
	address = {Tachikawa, Tokyo},
	title = {Sparse {Modeling} of {Graph}-{Structured} {Data} ... and ... {Images}},
	abstract = {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},
	language = {en},
	author = {Elad, Michael and Ram, Idan and Cohen, Israel},
	month = mar,
	year = {2014},
	note = {Virtual, Keynote Talk},
	keywords = {\#Graph, \#Sparse, \#Tutorial, \#Vision, /unread},
}

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