33 citations · 71 across the 4 of their papers we have counts for
5 papers
Efficient Solvers for Sparse Subspace Clustering
Farhad Pourkamali-Anaraki, James Folberth, Stephen Becker
Sparse subspace clustering (SSC) clusters points that lie near a union of low-dimensional subspaces. The SSC model expresses each point as a linear or affine combination of the…
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
Farhad Pourkamali-Anaraki, Stephen Becker
The Nystrom method is a popular technique that uses a small number of landmark points to compute a fixed-rank approximation of large kernel matrices that arise in machine learning…
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…
Efficient Dictionary Learning via Very Sparse Random Projections
Farhad Pourkamali-Anaraki, Stephen Becker, Shannon M. Hughes
Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionar…