10 citations · 14 across the 7 of their papers we have counts for
3 papers · 1 filter
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
Naichen Shi, Salar Fattahi, Raed Al Kontar
In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by…
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
Jiuyun Hu, Naichen Shi, Raed Al Kontar +1
We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets…
Personalized Dictionary Learning for Heterogeneous Datasets
Geyu Liang, Naichen Shi, Raed Al Kontar +1
We introduce a relevant yet challenging problem named Personalized Dictionary Learning (PerDL), where the goal is to learn sparse linear representations from heterogeneous datasets…