Agenda

Signal Processing Seminar

The Quest for Fast Learning from Few Examples

Andreas Loukas

Though the data in our disposal are numerous and diverse, deriving meaning from them is often non trivial. This talk centers on two key challenges of data analysis, relating to the sample complexity (how many examples suffice to learn something with statistical significance) and computational complexity (how long does the computation take) of learning algorithms. In particular, we are going to consider two famous unsupervised algorithms, principal component analysis and spectral clustering, and ask what can they learn when given very few examples or a fraction of the computation time.

Overview of Signal Processing Seminar