  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 scenario. This interesting relationship makes it
possible to establish distributed kernel PCA for feature-distributed cases from
ideas in distributed PCA in sample-distributed scenario. In 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.
