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Randomized Clustered Nystrom for Large-Scale Kernel Machines
Farhad Pourkamali-Anaraki, Stephen Becker
The Nystrom method has been popular for generating the low-rank approximation of kernel matrices that arise in many machine learning problems. The approximation quality of the Nyst…
A Randomized Approach to Efficient Kernel Clustering
Farhad Pourkamali-Anaraki, Stephen Becker
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory re…
Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
Aleksandr Y. Aravkin, Stephen Becker
We focus on the robust principal component analysis (RPCA) problem, and review a range of old and new convex formulations for the problem and its variants. We then review dual smoo…