no code implementations • 4 Jan 2024 • Xuan Ma, Jianhua Zhao, Changchun Shang, Fen Jiang, Philip L. H. Yu
This introduces two challenges for $t$fa: (i) the inherent matrix structure of the data is broken, and (ii) robustness may be lost, as vectorized matrix data typically results in a high data dimension, which could easily lead to the breakdown of $t$fa.
no code implementations • 13 Dec 2021 • Xuan Ma, Jianhua Zhao, Yue Wang
To solve the robustness problem suffered by FPCA and make it applicable to matrix data, in this paper we propose a robust extension of FPCA (RFPCA), which is built upon a $t$-type distribution called matrix-variate $t$ distribution.
no code implementations • 28 Jun 2021 • Tieyun Qian, Yile Liang, Qing Li, Xuan Ma, Ke Sun, Zhiyong Peng
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
no code implementations • 23 Mar 2021 • Xuan Ma, Xiaoshan Yang, Junyu Gao, Changsheng Xu
However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging.