# A Weighted K-Center Algorithm for Data Subset Selection

arXiv: https://arxiv.org/abs/2312.10602 · HTML: https://mlsanity.com/paper/2312.10602/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Deep Active Learning: Unified and Principled Method for Query and Training](https://mlsanity.com/paper/1911.09162/) (2019)
- [Task-Aware Variational Adversarial Active Learning](https://mlsanity.com/paper/2002.04709/) (2020)
- [SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios](https://mlsanity.com/paper/2107.00717/) (2021)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need](https://mlsanity.com/paper/1901.11409/) (2019)
- [Data Shapley Valuation for Efficient Batch Active Learning](https://mlsanity.com/paper/2104.08312/) (2021)
- [Adaptivity to Noise Parameters in Nonparametric Active Learning](https://mlsanity.com/paper/1703.05841/) (2017)
- [Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets](https://mlsanity.com/paper/1411.1752/) (2014)
- [Efficient Parallel Algorithms for k-Center Clustering](https://mlsanity.com/paper/1604.03228/) (2016)
- [Submodular Function Maximization for Group Elevator Scheduling](https://mlsanity.com/paper/1707.00617/) (2017)

## Built on by (most cited first, 9 of 9)

- [A CLIP-Powered Framework for Robust and Generalizable Data Selection](https://mlsanity.com/paper/2410.11215/) (2024)
- [When Dynamic Data Selection Meets Data Augmentation](https://mlsanity.com/paper/2505.03809/) (2025)
- [Data Agent: Learning to Select Data via End-to-End Dynamic Optimization](https://mlsanity.com/paper/2603.07433/) (2026)
- [On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions](https://mlsanity.com/paper/2402.16442/) (2024)
- [GIST: Greedy Independent Set Thresholding for Max-Min Diversification with Submodular Utility](https://mlsanity.com/paper/2405.18754/) (2024)
- [Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning](https://mlsanity.com/paper/2507.12750/) (2025)
- [LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition](https://mlsanity.com/paper/2512.22385/) (2025)
- [Adaptive Data Selection for Multi-Layer Perceptron Training: A Sub-linear Value-Driven Method](https://mlsanity.com/paper/2510.21286/) (2025)
- [Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training](https://mlsanity.com/paper/2605.14773/) (2026)

## Abstract (arXiv)

The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior methods are shown to achieve impressive results: (1) margin sampling that focuses on selecting points with high uncertainty, and (2) core-sets or clustering methods such as k-center for informative and diverse subsets. We are not aware of any work that combines these methods in a principled manner. To this end, we develop a novel and efficient factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions. To handle large datasets, we show a parallel algorithm to run on multiple machines with approximation guarantees. The proposed algorithm achieves similar or better performance compared to other strong baselines on vision datasets such as CIFAR-10, CIFAR-100, and ImageNet.