TechTalks from event: Sparse Representation and Low-rank Approximation

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Sparsity Session

  • Opening Remarks Authors: Organizers
  • Local Analysis of Sparse Coding in the Presence of Noise Authors: Rodolphe Jenatton (Invited Talk)
    A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only been a few theoretical arguments supporting these evidences. In particular, sparse coding, or sparse dictionary learning, relies on a non-convex procedure whose local minima have not been fully analyzed yet. In this paper, we consider a probabilistic model of sparse signals, and show that, with high probability, sparse coding admits a local minimum around the reference dictionary generating the signals. Our study takes into account the case of over complete dictionaries and noisy signals, thus extending previous work limited to noiseless settings and/or under-complete dictionaries. The analysis we conduct is non-asymptotic and makes it possible to understand how the key quantities of the problem, such as the coherence or the level of noise, are allowed to scale with respect to the dimension of the signals, the number of atoms, the sparsity and the number of observations.
  • Robust Sparse Analysis Regularization Authors: Gabriel Peyré (Invited Talk)
    In this talk I will detail several key properties of L1-analysis regularization for the resolution of linear inverse problems. Most previous theoretical works consider sparse synthesis priors where the sparsity is measured as the norm of the coefficients that synthesize the signal in a given dictionary. In contrast, the more general analysis regularization minimizes the L1 norm of the correlations between the signal and the atoms in the dictionary. The corresponding variational problem includes several well-known regularizations such as the discrete total variation, the fused lasso and sparse correlation with translation invariant wavelets. I will first study the variations of the solution with respect to the observations and the regularization parameter, which enables the computation of the degrees of freedom estimator. I will then give a sufficient condition to ensure that a signal is the unique solution of the analysis regularization when there is no noise in the observations. The same criterion ensures the robustness of the sparse analysis solution to a small noise in the observations. Lastly I will define a stronger condition that ensures robustness to an arbitrary bounded noise. In the special case of synthesis regularization, our contributions recover already known results, that are hence generalized to the analysis setting. I will illustrate these theoretical results on practical examples to study the robustness of the total variation, fused lasso and translation invariant wavelets regularizations. (This is joint work with S. Vaiter, C. Dossal, J. Fadili)
  • Dictionary-Dependent Penalties for Sparse Estimation and Rank Minimization Authors: David Wipf (Invited Talk)
    In the majority of recent work on sparse estimation algorithms, performance has been evaluated using ideal or quasi-ideal dictionaries (e.g., random Gaussian or Fourier) characterized by unit L2 norm, incoherent columns or features. But these types of dictionaries represent only a subset of the dictionaries that are actually used in practice (largely restricted to idealized compressive sensing applications). In contrast, herein sparse estimation is considered in the context of structured dictionaries possibly exhibiting high coherence between arbitrary groups of columns and/or rows. Sparse penalized regression models are analyzed with the purpose of finding, to the extent possible, regimes of dictionary invariant performance. In particular, a class of non-convex, Bayesian-inspired estimators with dictionary-dependent sparsity penalties is shown to have a number of desirable invariance properties leading to provable advantages over more conventional penalties such as the L1 norm, especially in areas where existing theoretical recovery guarantees no longer hold. This can translate into improved performance in applications such model selection with correlated features, source localization, and compressive sensing with constrained measurement directions. Moreover, the underlying methodology naturally extends to related rank minimization problems.
  • Group Sparse Hidden Markov Models Authors: Jen-Tzung Chien, Cheng-Chun Chiang

Low-rank Session