REVIEW 2 cited by
A variational approach to stable principal component pursuit
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations. For numerical solutions of our SPCP formulation, we first develop a convex variational framework and then accelerate it with quasi-Newton methods. We show, via synthetic and real data experiments, that our approach offers advantages over the classical SPCP formulations in scalability and practical parameter selection.
Forward citations
Cited by 2 Pith papers
-
Improved Turbo Message Passing for Compressive Robust Principal Component Analysis: Algorithm Design and Asymptotic Analysis
An improved turbo message-passing algorithm for compressive robust PCA, with state-evolution analysis that predicts its MSE dynamics and a sufficient condition for global convergence.
-
GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection
GaLore+ speeds up GaLore-style low-rank LLM fine-tuning roughly 4x by sharing projections across attention heads and using randomized SVD, with a sparse residual correction.
Discussion (0). Continue with ORCID to comment.