An important result on PCA recovery thresholds in low-rank matrix inference using the replica method, from our own @urteado and @PierpaoloVivo in collaboration with @gabrielescr . Check it out on the arXiv [arxiv.org/abs/2511.11927] 馃憞
arxiv.org Sparse corruption in low-rank matrix inference: the PCA benchmark 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 dense, homogeneous... 馃毃 NEW PHASE TRANSITION ALERT 馃毃View quoted post on X
We just posted a paper on arXiv that extends the famous BBP transition to the case of sparse noise!
The "ASV transition" shows when PCA can recover signals from sparse corruption - beyond classical random matrix theory. [arxiv.org/abs/2511.11927]
