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Subspace Clustering with Missing Data via Integer Programming In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n data points, arranged into columns of a…


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Lijun
Ding
Title: Low rank matrix optimization
Abstract: This talk
consists of two parts:
(1) semidefinite programming with low rank solution; (2)
statistical…




Principal components analysis (PCA) is a wellknown 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…


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…


ORIE/Statistics Colloquium: Sahand Negahban (MIT)  Structured Estimation in HighDimensions
Wednesday, February 20, 2013 at 4:15pm
Frank H. T. Rhodes Hall, 253
Modern techniques in data…


