Eigen Memory Trees

TL;DR

The paper introduces Eigen Memory Trees, an efficient online memory model for sequential learning that outperforms existing methods by routing data through a binary tree using principal components, validated on numerous datasets.

Contribution

It presents the Eigen Memory Tree, a novel data structure for online learning that improves memory access efficiency and performance over prior approaches.

Findings

Outperforms existing online memory models.

Validated on 206 datasets from OpenML.

Hybrid EMT-parametric algorithm shows significant improvements.

Abstract

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.