Sliced-Wasserstein Distance-based Data Selection

• URL: http://arxiv.org/abs/2504.12918v1
• Date: Thu, 17 Apr 2025 13:07:26 GMT
• Title: Sliced-Wasserstein Distance-based Data Selection
• Authors: Julien Pallage, Antoine Lesage-Landry,
• Abstract summary: We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance.<n>Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors.<n>We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learning approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.

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