## Section: New Results

### Recent results on sparse representations

Sparse approximation, high dimension, scalable algorithms, dictionary design, sample complexity

The team has had a substantial activity ranging from theoretical results to algorithmic design and software contributions in the field of sparse representations, which is at the core of the ERC project PLEASE (projections, Learning and Sparsity for Efficient Data Processing, see Section 9.2.1.1 ).

#### Theoretical results on sparse representations, graph signal processing, and dimension reduction

Participants : Rémi Gribonval, Yann Traonmilin, Gilles Puy, Nicolas Tremblay, Pierre Vandergheynst.

Main collaboration: Mike Davies (University of Edinburgh), Pierre Borgnat (ENS Lyon),

**Stable recovery of low-dimensional cones in Hilbert spaces:**
Many inverse problems in signal processing deal with the robust estimation of unknown data from underdetermined linear observations. Low dimensional models, when combined with appropriate regularizers, have been shown to be efficient at performing this task. Sparse models with the ${\ell}_{1}$-norm or low rank models with the nuclear norm are examples of such successful combinations. Stable recovery guarantees in these settings have been established using a common tool adapted to each case: the notion of restricted isometry property (RIP). This year, we established generic RIP-based guarantees for the stable recovery of cones (positively homogeneous model sets) with arbitrary regularizers. These guarantees were illustrated on selected examples. For block structured sparsity in the infinite dimensional setting, we used the guarantees for a family of regularizers which efficiency in terms of RIP constant can be controlled, leading to stronger and sharper guarantees than the state of the art. A journal paper is currently under revision [57] .

**Recipes for stable linear embeddings from Hilbert spaces to ${\mathbb{R}}^{m}$:**
We considered the problem of constructing a linear map from a Hilbert space (possibly infinite dimensional) to ${\mathbb{R}}^{m}$ that satisfies a restricted isometry property (RIP) on an arbitrary signal model set. We obtained a generic framework that handles a large class of low-dimensional subsets but also *unstructured* and *structured* linear maps. We provided a simple recipe to prove that a random linear map satisfies a general RIP on the model set with high probability. We also described a generic technique to construct linear maps that satisfy the RIP. Finally, we detailed how to use our results in several examples, which allow us to recover and extend many known compressive sampling results. This has been presented at the conference EUSIPCO 2015 [28] , and a journal paper has been submitted [55] .

**Random sampling of bandlimited signals on graphs:**
We studied the problem of sampling $k$-bandlimited signals on graphs. We proposed two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, no more than $O\left(klog\right(k\left)\right)$ measurements are sufficient to ensure an accurate and stable recovery of all $k$-bandlimited signals. This second strategy is based on a careful choice of the sampling distribution, which can be estimated quickly. Then, we proposed a computationally efficient decoder to reconstruct $k$-bandlimited signals from their samples. We proved that it yields accurate reconstructions and that it is also stable to noise. Finally, we conducted several experiments to test these techniques. A journal paper has been submitted [56] .

**Accelerated spectral clustering:**
We leveraged the proposed random sampling technique to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first $k$ eigenvectors of the similarity matrix' Laplacian, whose computation cost, even for sparse matrices, becomes prohibitive for large datasets. We showed that we can estimate the spectral clustering distance matrix without computing these eigenvectors: by graph filtering random signals. Also, we took advantage of the stochasticity of these random vectors to estimate the number of clusters $k$. We compared our method to classical spectral clustering on synthetic data, and show that it reaches equal performance while being faster by a factor at least two for large datasets. A conference paper has been accepted at ICASSP 2016 [43] and a long version is in preparation.

#### Algorithmic and theoretical results on dictionary learning

Participants : Rémi Gribonval, Luc Le Magoarou, Nicolas Bellot, Thomas Gautrais, Nancy Bertin, Srdan Kitic.

Main collaboration (theory for dictionary learning): Rodolphe Jenatton, Francis Bach (Equipe-projet SIERRA (Inria, Paris)), Martin Kleinsteuber, Matthias Seibert (TU-Munich),

**Theoretical guarantees for dictionary learning :** An important practical problem in sparse modeling is to choose the
adequate dictionary to model a class of signals or images of interest.
While diverse heuristic techniques have been proposed in the litterature to learn a dictionary
from a collection of training samples, there are little existing results which provide an adequate
mathematical understanding of the behaviour of these techniques and their ability to recover an ideal
dictionary from which the training samples may have been generated.

Beyond our pioneering work [86] , [109] [5] on this topic, which concentrated on the noiseless case for non-overcomplete dictionaries, we showed the relevance of an ${\ell}^{1}$ penalized cost function for the locally stable identification of overcomplete incoherent dictionaries, in the presence of noise and outliers [19] . Moreover, we established sample complexity bounds of dictionary learning and other related matrix factorization schemes (including PCA, NMF, structured sparsity ...) [20] .

**Learning computationally efficient dictionaries** Classical dictionary learning is limited to small-scale problems. Inspired by usual fast transforms, we proposed a general dictionary structure that allows cheaper manipulation, and an algorithm to learn such dictionaries –and their fast implementation. The principle and its application to image denoising appeared at ICASSP 2015 [33] and an application to speedup linear inverse problems was published at EUSIPCO 2015 [32] . A journal paper is currently under revision [51] .

We further explored the application of this technique to obtain fast approximations of Graph Fourier Transforms – a conference paper on this latter topic has been accepted for publication in ICASSP 2016 [41] . A C++ software library is in preparation to release the resulting algorithms.

**Operator learning for cosparse representations:**
Besides standard dictionary learning, we also considered learning in the context of the cosparse model.
The overall problem is to learn a low-dimensional signal model from a collection of training samples.
The mainstream approach is to learn an overcomplete dictionary to provide good approximations of
the training samples using sparse synthesis coefficients. This famous sparse model has a less well known
counterpart, in analysis form, called the cosparse analysis model. In this new model, signals are
characterized by their parsimony in a transformed domain using an overcomplete analysis operator.

This year we obtained an upper bound of the sample complexity of the learning process for analysis operators, and designed a stochastic gradient descent (SGD) method to efficiently learn analysis operators with separable structures. Numerical experiments were provided that link the sample complexity to the convergence speed of the SGD algorithm. A journal paper has been published [24] .

#### An alternative framework for sparse representations: analysis sparse models

Participants : Rémi Gribonval, Nancy Bertin, Srdan Kitic, Laurent Albera.

In the past decade there has been a great interest in a synthesis-based model for signals, based on
sparse and redundant representations. Such a model assumes that the signal of interest can be composed
as a linear combination of *few* columns from a given matrix (the dictionary). An alternative
*analysis-based* model can be envisioned, where an analysis operator multiplies the signal,
leading to a *cosparse* outcome.
Building on our pioneering work on the cosparse model [101] , [85] , [102]
successful applications of this approach to sound source localization, audio declipping and brain imaging have been developed this year.

**Versatile co-sparse regularization:** Digging the groove of last year results (comparison of the performance of several cosparse recovery algorithms in the context of sound source localization [94] , demonstration of its efficiency in situations where usual methods fail ( [96] , see paragraph
7.5.2 ), applicability to the hard declipping problem [95] , application to EEG brain imaging [60] (see paragraph
7.5.3 ), a journal paper embedding the latest algorithms and results in sound source localization and brain source localization in a unified fashion was published in IEEE Transactions on Signal Processing [23] . Other communications were made in conferences and workshops [50] , [31] and Srdan Kitic defended his PhD thesis [12] . New results include experimental confirmation of robustness and versatility of the proposed scheme, and of its computational merits (convergence speed increasing with the amount of data)

**Parametric operator learning for cosparse calibration:**
In many inverse problems, a key challenge is to cope with unknown physical parameters of the problem such as the speed of sound or the boundary impedance. In the sound source localization problem, we showed that the unknown speed of sound can be learned jointly in the process of cosparse recovery, under mild conditions (work presented last year at iTwist'14 workshop [66] ). This year, improved and extended results were obtained: first with a new algorithm for sound source localization with unknown speed of sound [12] , then by extending the formulation to the case of unknown boundary impedance, and showing that a similar biconvex formulation and optimization could solve this new problem efficiently (conference paper accepted for publication in ICASSP 2016 [38] , see also Section
7.3.2 ).