Distributed Kernel-Based Gradient Descent Algorithms

• Published in: Constructive approximation Vol. 47; no. 2; pp. 249 - 276
• Main Authors: Lin, Shao-Bo, Zhou, Ding-Xuan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2018; Springer Nature B.V
• Subjects: Algorithms; Analysis; Hilbert space; Machine learning; Mathematics; Mathematics and Statistics; Numerical Analysis; Operators (mathematics); Statistical analysis; Integral operator; 41A35; Distributed learning; 68T05; Gradient descent algorithm; Learning theory; 94A20

We study the generalization ability of distributed learning equipped with a divide-and-conquer approach and gradient descent algorithm in a reproducing kernel Hilbert space (RKHS). Using special spectral features of the gradient descent algorithms and a novel integral operator approach, we provide o...