"PLEASE: Projections, Learning, and Sparsity for Efficient data-processing"

Final Report Summary - PLEASE (PLEASE: Projections, Learning, and Sparsity for Efficient data-processing)

Sparse models are at the core of many research domains where the large amount and high-dimensionality of digital data requires concise data descriptions for efficient information processing. The ability of these models to provide concise descriptions of complex data collections, together with algorithms of provable performance and bounded complexity, has been largely demonstrated.
A flagship application of sparsity is the paradigm of compressed sensing, which exploits sparsity for data acquisition using limited resources (e.g. fewer/less expensive sensors, limited energy consumption, etc.). Besides sparsity, a key pillar of compressed sensing is the use of random low-dimensional projections.

Sparse models and random projections are at the heart of many success stories in signal processing and machine learning. A major outcome of PLEASE is the demonstration of their high potential for resource-efficient learning at the era of Big Data, through the new concept of Compressive Learning. A particular example is Compressive Clustering, which makes it possible to cluster large collections (of typically tens of gigabytes or more) of training examples using a single concise ”summary” –called sketch– which size (typically only a few kilobytes) is independent of the number of examples, offering substantial memory savings.

With applications from audio processing and enhancement to brain imaging, PLEASE establishes a unifying framework to handle information from the sensing level to the semantic level through a series of mathematical, algorithmic, and experimental contributions that are deeply rooted in applied mathematics, statistics, and computer science.

Final Report Summary - PLEASE (PLEASE: Projections, Learning, and Sparsity for Efficient data-processing)

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