# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

arXiv: https://arxiv.org/abs/2101.01441 · HTML: https://mlsanity.com/paper/2101.01441/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Measuring the Intrinsic Dimension of Objective Landscapes](https://mlsanity.com/paper/1804.08838/) (2018)
- [Spectral Metric for Dataset Complexity Assessment](https://mlsanity.com/paper/1905.07299/) (2019)

## Built on by (most cited first, 3 of 3)

- [Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning](https://mlsanity.com/paper/2407.15724/) (2024)
- [Robust OOD Graph Learning via Mean Constraints and Noise Reduction](https://mlsanity.com/paper/2506.19281/) (2025)
- [Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction](https://mlsanity.com/paper/2609.11041/) (2026)

## Abstract (arXiv)

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.