arXiv Machine Learning July 31, 2026 By Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva, Luis Gustavo Nonato

FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction

Read the original on arXiv Machine Learning →

arXiv:2607. 27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 30

FloDR: An invertible dimensionality reduction method based on a normalising flow

arXiv:2607. 26278v1 Announce Type: new Abstract: It is common for two-dimensional embeddings of high-dimensional data to be read far beyond what they can support.

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arXiv Machine Learning

Jul 30

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting

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FloDR: An invertible dimensionality reduction method based on a normalising flow

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