Thomas A. Goldstein

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Computing with large kernel or similarity matrices is essential to many state-of-the-art machine learning techniques in classification, clustering, and dimension-ality reduction. The cost of forming and factoring these kernel matrices can become intractable for large datasets. We introduce an an adaptive column sampling technique called Accelerated(More)
—Sparse approximations using highly over-complete dictionaries is a state-of-the-art tool for many imaging applications including denoising, super-resolution, compressive sensing, light-field analysis, and object recognition. Unfortunately, the applicability of such methods is severely hampered by the computational burden of sparse approximation: these(More)
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