# Dictionary-Learning-Based Data Pruning for System Identification

arXiv: https://arxiv.org/abs/2502.11484 · HTML: https://mlsanity.com/paper/2502.11484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [Accelerating Deep Learning with Dynamic Data Pruning](https://mlsanity.com/paper/2111.12621/) (2021)
- [Data pruning and neural scaling laws: fundamental limitations of score-based algorithms](https://mlsanity.com/paper/2302.06960/) (2023)
- [Fast Low-rank Shared Dictionary Learning for Image Classification](https://mlsanity.com/paper/1610.08606/) (2016)
- [LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices](https://mlsanity.com/paper/2406.08765/) (2024)
- [Orthogonal Least Squares Based Fast Feature Selection for Linear Classification](https://mlsanity.com/paper/2101.08539/) (2021)
- [Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification](https://mlsanity.com/paper/2412.10091/) (2024)
- [Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring](https://mlsanity.com/paper/2106.08247/) (2021)

## Abstract (arXiv)

System identification is normally involved in augmenting time series data by time shifting and nonlinearisation (e.g., polynomial basis), both of which introduce redundancy in features and samples. Many research works focus on reducing redundancy feature-wise, while less attention is paid to sample-wise redundancy. This paper proposes a novel data pruning method, called mini-batch FastCan, to reduce sample-wise redundancy based on dictionary learning. Time series data is represented by some representative samples, called atoms, via dictionary learning. The useful samples are selected based on their correlation with the atoms. The method is tested on one simulated dataset and two benchmark datasets. The R-squared between the coefficients of models trained on the full datasets and the coefficients of models trained on pruned datasets is adopted to evaluate the performance of data pruning methods. It is found that the proposed method significantly outperforms the random pruning method.