An analysis of sketched IRLS for accelerated sparse residual regression

• Main Authors: IWATA, Daichi, WAECHTER, Michael, LIN, Wen-yan, MATSUSHITA, Yasuyuki
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Sparse residual regression; L1 minimization; Randomized algorithm; Matrix sketching; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/6110; https://ink.library.smu.edu.sg/context/sis_research/article/7113/viewcontent/123570596.pdf
• Institution: Singapore Management University

This paper studies the problem of sparse residual regression, i.e., learning a linear model using a norm that favors solutions in which the residuals are sparsely distributed. This is a common problem in a wide range of computer vision applications where a linear system has a lot more equations than...