Systems | Information | Learning | Optimization
 

Restricted Isometry Constants in Compressed Sensing | Network localization with some new wrinkles: Noncovex geometry in low dimension

Bubacarr’s talk: ABSTRACT: Restricted Isometry Constants (RICs) of a matrix are a popular tool in the analysis of compressed sensing algorithms. The best known bounds will be presented for Gaussian matrices as well as expander graphs. In the former case we will also present explicit formulae for the bounds in …

An optimal architecture for poset-causal systems | Computable Bounds on Sparsity Recovery

Gongguo’s talk: Title: Computable Bounds on Sparsity Recovery Abstract: The performance of sparsity recovery depends on the structures of the sensing matrices. The quality of these matrices in the context of signal recovery is usually quantified by the restricted isometry constant and its variants. However, the restricted isometry constant and …

Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities

This talk will follow up on last week’s SILO talk by Prof. Ellenberg. In particular, this will be a discussion about the “EDSN” algorithm for robust and efficient hierarchical clustering. Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in …

Audio Morphing

This talk introduces a way to conduct audio morphings by imposing a constraint that can be used to smoothly connect two different audio spectra. The method exploits a formal analogy between the two spatial dimensions of Laplace’s partial differential equation and the two dimensions (time and frequency) of a spectrogram. …

Online Subspace Estimation and Tracking from Missing or Corrupt Data

Low-dimensional linear subspace approximations to high-dimensional data are a common approach to handling problems of estimation, detection and prediction, with applications such as network monitoring, collaborative filtering, object tracking in computer vision, and environmental sensing. Corrupt and missing data are the norm in many high-dimensional situations, not only because of …