# Lifelong Metric Learning

## TL;DR

This paper introduces Lifelong Metric Learning (LML), a framework enabling models to learn new metrics incrementally while retaining previous knowledge, inspired by human learning, and optimized via online algorithms.

## Contribution

The paper proposes a novel lifelong metric learning framework that maintains a shared subspace, transfers knowledge to new tasks, and updates over time, advancing online multi-task metric learning.

## Key findings

- Effective in multi-task metric learning datasets
- Maintains performance across tasks over time
- Demonstrates efficiency and effectiveness

## Abstract

The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human learning", i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating previous experiences and knowledge. Therefore, we propose a new metric learning framework: lifelong metric learning (LML), which only utilizes the data of the new task to train the metric model while preserving the original capabilities. More specifically, the proposed LML maintains a common subspace for all learned metrics, named lifelong dictionary, transfers knowledge from the common subspace to each new metric task with task-specific idiosyncrasy, and redefines the common subspace over time to maximize performance across all metric tasks. For model optimization, we apply online passive aggressive optimization algorithm to solve the proposed LML framework, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multi-task metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.