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Introduction

The emergence of new high-volume data acquisition technologies has created unprecedented amounts of scientific and social data that calls for developing specialized data acquisition and analysis, data mining and learning tools. Petabytes of data from high-throughput sequencers, recommender systems, complex imaging devices, astronomical observatories, and systems such as the Large Hadron Collider, all require sophisticated solutions for information capture, compression, dimensionality-reduction, and compressive computing. The goals of this symposium are twofold: to identify the unique challenges posed by static and dynamic Big Data formats arising in practical applications and to provide an interdisciplinary platform for the exchange of ideas among statisticians, engineers, and experts in signal processing, machine learning and communication theory.

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Submission Topics

The symposium is structured around three focused tracks: Track 1: Real-time data capture and compression methods Gigapixel imaging and hyper-spectral imaging, Synthetic Aperture Radar Time stretch imaging, spectroscopy and real-time data compression Image compression, Anamorphic data compression, digital pathology and telemedicine Track 2: Theory and algorithms for dynamic sparse and/or low rank recovery. Dynamic Compressed Sensing, Sparse Recovery Dynamic Robust PCA, Dynamic Matrix Completion Applications to dynamic medical imaging, video analysis, cognitive radios, machine learning Track 3: Subspace Methods for High-Dimensional Data. Subspace and Covariance estimation, possibly with missing and corrupted data Unions-of-subspaces and non-linear manifold models Machine learning with subspace methods
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Important Date

  • Conference Date

    Dec 03

    2014

    to

    Dec 05

    2014

  • Dec 05 2014

    Registration deadline

Sponsored By
IEEE Signal Processing Society
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