I’m posting this just for the purpose of timestamping. Today I proposed an idea to stream kernel learning techniques such as semidefinite embedding. The trick is to pass minors one row and column at a time (actually, the matrix is symmetric, so just one row) and update using incremental kernel PCA. This results in an algorithm that only needs to store N elements in memory at a time rather than N^2.