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Low-rank modeling

Low-rank modeling

This video was recorded at Machine Learning Summer School (MLSS ), Bordeaux 2011. Inspired by the success of compressive sensing, the last three years have seen an explosion of research in the theory of low-rank modeling. By now, we have results stating that it is possible to recover certain low-rank matrices from a minimal number of entries -- or of linear functionals -- by tractable convex optimization. We further know that these methods are robust vis a vis additive noise and even outliers. In a different direction, researchers have developed computationally tractable methods for clustering high-dimensional data points that are assumed to be drawn from multiple low-dimensional linear subspaces. This talk will survey some exciting results in these areas.


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