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ORIE 9000 Colloquium - Madeleine Udell: Generalized Low Rank Models

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets…

From  E. Cornelius 5 plays

CAM Colloquium September 19, 2014 - Julianne Chung: Designing Optimal Spectral Filters and Low-Rank Matrices for Inverse Problems

Computing reliable solutions to inverse problems is important in many applications such as biomedical imaging, computer graphics, and security. Regularization by incorporating prior knowledge is…

From  E. Cornelius 30 plays

CAM Colloquium, 2014-09-19 - Julianne Chung: Designing Optimal Spectral Filters and Low-rank Matrices for Inverse Problems

From  E. Cornelius 71 plays