Finding models with a low-dimensional structure, given a number of linear observations much smaller than the ambient dimension, has been well-studied in recent years. Examples of such models are sparse vectors, low-rank matrices, and the sum of sparse and low-rank matrices, among others. In many signal processing and machine learning …
Many applications concern sparse signals, for example, detecting anomalies from the differences between consecutive images taken by surveillance cameras. In general, anomaly events are sparse. This talk focuses on the problem of recovering a K-sparse signal in N dimensions (coordinates). Classical theories in compressed sensing say the required number of …
Gautam’s Talk: TItle: Active Learning on Graphs Label prediction on graphs, i.e., the prediction of the labels of the vertices of a given graph using the labels of a subset of vertices, is a problem that commonly occurs in many areas of machine learning and data analysis. In this talk …
We consider multistage adaptive sensing of signals and spectra where the components of interest are sparse. The advantage of adaptation in this context is the ability to focus more of the sensing budget on regions where signal components exist, thereby improving the signal-to-noise ratio. A dynamic programming formulation is derived …