4 papers
Transformed Regularizations for Robust Principal Component Analysis: Toward a Fine-Grained Understanding
Kun Zhao, Haoke Zhang, Jiayi Wang +1
Robust Principal Component Analysis (RPCA) aims to recover a low-rank structure from noisy, partially observed data that is also corrupted by sparse, potentially large-magnitude ou…
Noisy Low-Rank Matrix Completion via Transformed Regularization and its Theoretical Properties
Kun Zhao, Jiayi Wang, Yifei Lou
This paper focuses on recovering an underlying matrix from its noisy partial entries, a problem commonly known as matrix completion. We delve into the investigation of a non-convex…
A comparative analysis of rank aggregation methods for the partial label ranking problem
Jiayi Wang, Juan C. Alfaro, Viktor Bengs
The label ranking problem is a supervised learning scenario in which the learner predicts a total order of the class labels for a given input instance. Recently, research has incre…
Low-Rank Phase Retrieval via Variational Bayesian Learning
Kaihui Liu, Jiayi Wang, Zhengli Xing +2
In this paper, we consider the problem of low-rank phase retrieval whose objective is to estimate a complex low-rank matrix from magnitude-only measurements. We propose a hierarchi…