Lifelong Metric Learning

By Gan Sun, Cong Yang, Ji Liu, Lianqing Liu, Xiaowei Xu, Haibin Yu. 2017

28 citations recorded by the source corpus.

The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a 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 the previous experiences. 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 learn each new metric learning 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 achieve lifelong metric task learning, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multitask metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Large Scale Incremental Learning (2019, background): “[25, 26] updated knowledge dictionary for new tasks and kept dictionary coefficients for old tasks.”