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Related Experiment Video

Updated: Oct 28, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Efficient Adaptive Online Learning via Frequent Directions.

Yuanyu Wan, Lijun Zhang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 14, 2021

Summary

Adaptive subgradient methods (ADAGRAD) are improved for large-scale online learning. New variants, ADA-FD and ADA-FFD, use matrix sketching for efficiency while maintaining performance.

Area of Science:

Machine Learning
Optimization Algorithms

Background:

Adaptive subgradient methods (ADAGRAD) are crucial for online learning and optimization.
Full matrix proximal functions in ADAGRAD (ADA-FULL) face scalability issues due to high computational complexity (O(d^3) time, O(d^2) space).

Purpose of the Study:

To develop efficient variants of ADA-FULL for large-scale problems.
To maintain the performance benefits of ADA-FULL when gradients are correlated.

Main Methods:

Proposed two variants: ADA-FD and ADA-FFD, utilizing the frequent directions (FD) matrix sketching technique.
ADA-FD directly uses FD to manage low-rank matrices, reducing complexity to O(τd) space and O(τ^2d) time.
ADA-FFD accelerates FD with a doubling trick, achieving O(τd) average time complexity at the cost of doubled space complexity.

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Main Results:

Theoretical analysis shows ADA-FD and ADA-FFD achieve regret comparable to ADA-FULL for low-rank gradient matrices.
Experimental results validate the efficiency and effectiveness of the proposed ADA-FD and ADA-FFD algorithms.

Conclusions:

ADA-FD and ADA-FFD offer significant improvements in scalability for ADAGRAD methods.
Matrix sketching provides a practical approach to leverage the benefits of full matrix methods in large-scale settings.