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info.json
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{
"abstract": "We consider a general trace regression model with multiple structural changes and propose a universal approach for simultaneous exact or near-low-rank matrix recovery and change-point detection. It incorporates nuclear norm penalized least-squares minimization into a grid search scheme that determines the potential structural break. Under a set of general conditions, we establish the non-asymptotic error bounds with a nearly-oracle rate for the matrix estimators as well as the super-consistency rate for the change-point localization. We use concrete random design instances to justify the appropriateness of the proposed conditions. Numerical results demonstrate the validity and effectiveness of the proposed scheme.",
"authors": [
"Lei Shi",
"Guanghui Wang",
"Changliang Zou"
],
"emails": [
"leishi@berkeley.edu",
"ghwang.nk@gmail.com",
"nk.chlzou@gmail.com"
],
"id": "22-0852",
"issue": 220,
"pages": [
1,
71
],
"title": "Low-Rank Matrix Estimation in the Presence of Change-Points",
"volume": 25,
"year": 2024
}