# RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection

arXiv: https://arxiv.org/abs/2509.07523 · HTML: https://mlsanity.com/paper/2509.07523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 9 Sep 2025.

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

- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Deep K-SVD Denoising](https://mlsanity.com/paper/1909.13164/) (2019)
- [Convolutional Dictionary Learning via Local Processing](https://mlsanity.com/paper/1705.03239/) (2017)
- [Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals](https://mlsanity.com/paper/1805.09654/) (2018)
- [Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders](https://mlsanity.com/paper/1807.04734/) (2018)
- [Transforming Blood Cell Detection and Classification with Advanced Deep Learning Models: A Comparative Study](https://mlsanity.com/paper/2410.15670/) (2024)

## Abstract (arXiv)

Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis.