  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.
