  This work introduces the Eigen Memory Tree (EMT), a novel online memory model
for sequential learning scenarios. EMTs store data at the leaves of a binary
tree and route new samples through the structure using the principal components
of previous experiences, facilitating efficient (logarithmic) access to
relevant memories. We demonstrate that EMT outperforms existing online memory
approaches, and provide a hybridized EMT-parametric algorithm that enjoys
drastically improved performance over purely parametric methods with nearly no
downsides. Our findings are validated using 206 datasets from the OpenML
repository in both bounded and infinite memory budget situations.
