LDMO 2013 - 2013 Symposium on: Low-Dimensional Models and Optimization in Signal Processing

Sparsity, low-rank, and other low-dimensional geometric models have long been studied and exploited in machine learning, signal processing and computer science. For instance, sparsity has made an impact in the problems of compression, linear regression, subset selection, graphical model learning, and compressive sensing. Similarly, low-rank models lie at the heart of a variety of dimensionality reduction and data interpolation techniques. Additionally, manifold models are able to succinctly characterize complex signal classes that exhibit few degrees of freedom.
The goal of this symposium is to showcase recent developments in the formulation of: (i) new low-dimensional models for high-dimensional data that are concise enough to be informative in signal recovery and information extraction, where examples include sparsity, low-rank matrices, and manifold models; (ii) new signal recovery and information extraction algorithms, in particular optimization-based approaches, that promote solutions well-matched to the aforementioned signal models, where examples include greedy and iterative algorithms, optimization solvers, and heuristic methods; and (iii) new applications where where low-dimensional signal models outperform existing signal processing, signal estimation, and data recovery approaches in the computational, fidelity, or measurement requirement realms.
Submissions of at most 1 page in two-column IEEE format are welcome on topics including:
Dimensionality Reduction
Algorithms for Signal Processing
Signal Models
Signal Processing
Compressive Sensing