Last week we heard about the first of our keynote speakers at this years’ iTWIST workshop in August – Lieven Vandenberghe.

Next up on my list of speakers is Karin Schnass. Karin Schnass is an expert on dictionary learning and heading an FWF-START project on dictionary learning in the Applied Mathematics group in the Department of Mathematics at the University of Innsbruck.

Karin Schnass joined the University of Innsbruck in December 2014 as part of an Erwin Schrödinger Research Fellowship where she returned from a research position at University of Sassari, Italy, from 2012 to 2014. She originally graduated from University of Vienna, Austria, with a master in mathematics with distinction: *“Gabor Multipliers – A Self-Contained Survey”*. She graduated in 2009 with a PhD in computer, communication and information sciences from EPFL, Switzerland: *“Sparsity & Dictionaries – Algorithms & Design”*. Karin Schnass has, among other things, introduced the iterative thresholding and K-means (ITKM) algorithms for dictionary learning and published the first theoretical paper on dictionary learning (on arXiv) with Rémi Gribonval.

At our workshop this August, I am looking forward to hearing Karin Schnass talk about Sparsity, Co-sparsity and Learning. In compressed sensing, the so-called synthesis model has been the prevailing model since the beginning. First, we have the measurements:

From the measurements, we can reconstruct the sparse vector *x* by solving this convex optimisation problem:

If the vector *x* we can observe is not sparse, we can still do this if can find a sparse representation *α* of *x* in some dictionary *D*:

where we take our measurements of *x* using some measurement matrix *M*:

and we reconstruct the sparse vector *α* as follows:

The above is called the synthesis model because it works by using some sparse vector *α* to *synthesize* the vector *x* that we observe. There is an alternative to this model, called the analysis model, where we *analyse* an observed vector *x* to find some sparse representation *β* of it:

Here *D’* is also a dictionary, but it is not the same dictionary as in the synthesis case. We can now reconstruct the vector *x* from the measurements *y* as follows:

Now if *D* is a (square) orthonormal matrix such as an IDFT, we can consider *D’* a DFT matrix and they are simply each other’s inverse. In this case, the synthesis and analysis reconstruction problems above are equivalent. The interesting case is when the synthesis dictionary *D* is a so-called over-complete dictionary – a fat matrix. The analysis counterpart of this is a tall analysis dictionary *D’* which behaves differently than the analysis dictionary.

Karin will give an overview over the synthesis and the analysis model and talk about how to learn dictionaries that are useful for either case. Specifically, she plans to tell us about (joint work with Michael Sandbichler):

While (synthesis) sparsity is by now a well-studied low complexity model for signal processing, the dual concept of (analysis) co-sparsity is much less invesitigated but equally promising. We will first give a quick overview over both models and then turn to optimisation formulations for learning sparsifying dictionaries as well as co-sparsifying (analysis) operators. Finally we will discuss the resulting learning algorithms and ongoing research directions.