Active Metric Learning for Supervised Classification
This work addresses the need for more efficient and generalizable metric learning methods in supervised classification, with potential applications in domains like image analysis and medical diagnostics, though it appears incremental by building on existing metric learning frameworks.
The paper tackles the problem of learning optimal distance metrics for classification by introducing mixed-integer optimization approaches that generalize the Mahalanobis metric and remove reliance on pre-specified data structures like target neighbors. It also enables active learning by recommending precise sampling regions to improve classification performance, demonstrating results on real image and medical datasets.
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated "target neighbors," "triplets," and "similarity pairs." Another salient feature of our method is its ability to enable active learning by recommending precise regions to sample after an optimal metric is computed to improve classification performance. This targeted acquisition can significantly reduce computational burden by ensuring training data completeness, representativeness, and economy. We demonstrate classification and computational performance of the algorithms through several simple and intuitive examples, followed by results on real image and medical datasets.