Title: Robust Principal Component Analysis

Speaker: Chae Clark (UMD)

Hyperspectral datasets have become pervasive in the field of signal processing. To process this data, we need efficient representations that require little space to store and transmit. Two major methods for accomplishing this are low-rank methods that map the dataset to a lower-dimensional space, and sparsifying methods that lessen the support of a dataset. The combination of these methods is the subject of this talk. Robust PCA attempts to decompose a dataset into sparse and low-rank components, resulting in efficient representations of the dataset.