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arXiv Machine Learning By Ghurumuruhan Ganesan

Criticality in Dissimilar Decomposition and Undersampling of Random Datasets with Anomalies

Read the original on arXiv Machine Learning →

The paper investigates how AI‑generated data, treated as anomalies linked to main data points, influences batch decompositions and undersampling in random datasets. Using redundancy graphs and iterative methods, it derives bounds for the minimum size of a strongly dissimilar decomposition and shows a phase transition: when anomalies are few, the minimum size depends on main data, but beyond a threshold it is dominated by anomalies. Additionally, the authors present a size criticality result for strong similarity in randomly undersampled datasets, illustrated with categorical data examples where the overall space far exceeds the dataset size.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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