Sparse Recovery

Research Highlights of
Information Processing & Algorithms Laboratory


The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL broadly encompasses signal and image processing theory and applications with a particular focus on capturing practical real-world constraints via convex optimization theory and algorithms.


ICR: Iterative Convex Refinement for Sparse Recovery

Here, we address sparse signal recovery in a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the setup of Yen et al. who employ a variant of spike and slab prior to encourage sparsity. The optimization problem resulting from this model has broad applicability in recovery and regression problems and is known to be a hard non-convex problem whose existing solutions involve simplifying assumptions and/or relaxations.

AMP: Adaptive matching pursuit for sparse signal recovery

Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coefficient vector to maximize these priors results in a hard non-convex and mixed integer programming problem.

LRSDL - Fast low-rank shared dictionary learning for object classification

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR).Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints.

ALSF - Analysis-Synthesis learning with shared features for histopathological image classification

The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. We introduce the learning of a low rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes.We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance trade-off.


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