Year: 2021
Type: article
Abstract: This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenarios. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas of distributed PCA in the sample-distributed scenario. In the theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high-quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice.
Source: IEEE Signal Processing Letters
Authors: Fan He, Kexin Lv, Jie Yang, Xiaolin Huang
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2403964263, G7523228654, G8081038515
OpenAlex ID: W3158501495