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Scalable Robust Matrix Factorization with Nonconvex Loss
Quanming Yao, James T. Kwok
Robust matrix factorization (RMF), which uses the -loss, often outperforms standard matrix factorization using the -loss, particularly when outliers are present. Th…
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Quanming Yao, James T. Kwok, Taifeng Wang +1
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of noncon…
Scalable Online Convolutional Sparse Coding
Yaqing Wang, Quanming Yao, James T. Kwok +1
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are…
Multi-Label Learning with Global and Local Label Correlation
Yue Zhu, James T. Kwok, Zhi-Hua Zhou
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all…
Collaborative Filtering with Social Local Models
Huan Zhao, Quanming Yao, James T. Kwok +1
Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restric…
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion
Quanming Yao, James T. Kwok
Matrix and tensor completion aim to recover a low-rank matrix / tensor from limited observations and have been commonly used in applications such as recommender systems and multi-r…