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Embeddings provide compact representations of signals to be used to perform inference in a wide variety of tasks. Random projections have been extensively used to preserve Euclidean distances or inner products of high dimensional signals into low dimensional representations. Different techniques based on hashing have been used in the past to embed set similarity metrics such as the Jaccard coefficient...
In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate...
In this paper, we present a new recovery algorithm for innetwork compressed sensing from measurements acquired in multi-agent systems. Each agent has to recover a common signal taking advantage of local communication and simple computations. Such distributed problem typically incurs a high energy cost due to inter-node communications. In this paper we propose an iterative distributed algorithm to...
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