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stat.ML2025
Sparse corruption in low-rank matrix inference: the PCA benchmark
Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo
Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations. It is known that applying PCA to a rank-one signal corrupted by a de…
stat.ML2023★ 4 cited
Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo
We characterise the learning of a mixture of two clouds of data points with generic centroids via empirical risk minimisation in the high dimensional regime, under the assumptions…