# Sparse Compositional Metric Learning

arXiv: https://arxiv.org/abs/1404.4105 · HTML: https://mlsanity.com/paper/1404.4105/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2014.

## Built on by (most cited first, 5 of 5)

- [Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later](https://mlsanity.com/paper/2407.03257/) (2024)
- [Robustness Implies Generalization via Data-Dependent Generalization Bounds](https://mlsanity.com/paper/2206.13497/) (2022)
- [Gentle Local Robustness implies Generalization](https://mlsanity.com/paper/2412.06381/) (2024)
- [Adaptive neighborhood Metric learning](https://mlsanity.com/paper/2201.08314/) (2022)
- [Distance Metric Learning through Minimization of the Free Energy](https://mlsanity.com/paper/2106.05495/) (2021)

## Abstract (arXiv)

We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.