2017/05/29 by Agniva Chowdhury, Chowdhury, Agniva, Jiasen Yang +3
Computer Science · Engineering · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.1705.10102
openalex publication_date 2017/05/29 · openalex created_date 2022/08/13 · openalex updated_date 2026/07/28
Projection-cost preservation is a low-rank approximation guarantee which\nensures that the cost of any rank-k projection can be preserved using a\nsmaller sketch of the original data matrix. We present a general structural\nresult outlining four sufficient conditions to achieve projection-cost\npreservation. These conditions can be satisfied using tools from the Randomized\nLinear Algebra literature.\n