Abstract
When problems are scaled to "big data," researchers must often come up with new solutions, leveraging ideas from multiple research areas - as we frequently witness in today's big data techniques and tools for machine learning, bioinformatics, and data visualization. Beyond these heavily studied topics, there exist other classes of general problems that need to be rethought at scale. One such problem is that of large-scale signal reconstruction [4]: taking a set of observations of relatively low dimensionality, and using them to reconstruct a high-dimensional, unknown signal. This class of problems arises when we can only observe a subset of a complex environment that we are seeking to model - for instance, placing a few sensors and using their readings to reconstruct an environment's temperature, or monitoring multiple points in a network and using the readings to estimate end-to-end network traffic, or using 2D slices to reconstruct a 3D image.
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