no code implementations • 1 Dec 2021 • Shuai Shao, Lei Xing, Rui Xu, Weifeng Liu, Yan-Jiang Wang, Bao-Di Liu
Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model.
no code implementations • 16 Sep 2021 • Shuai Shao, Lei Xing, Yan Wang, Rui Xu, Chunyan Zhao, Yan-Jiang Wang, Bao-Di Liu
Apply the trained FEM to acquire the novel data's features and recognize them.
no code implementations • 7 Sep 2021 • Shuai Shao, Lei Xing, Yixin Chen, Yan-Jiang Wang, Bao-Di Liu, Yicong Zhou
(2) Use the FEM to extract the features of novel data (with few labeled samples and totally different categories from base data), then classify them with the to-be-designed classifier.
no code implementations • 23 Oct 2020 • Shuai Shao, Mengke Wang, Rui Xu, Yan-Jiang Wang, Bao-Di Liu
To tackle this issue, we propose a Dynamic Label Dictionary Learning (DLDL) algorithm to generate the soft label matrix for unlabeled data.
no code implementations • 23 Oct 2020 • Shuai Shao, Rui Xu, Yan-Jiang Wang, Weifeng Liu, Bao-Di Liu
In this paper, we propose a hypergraph based sparse attention mechanism to tackle this issue and embed it into dictionary learning.
no code implementations • 17 Apr 2019 • Yan-Jiang Wang, Shuai Shao, Rui Xu, Werifeng Liu, Bao-Di Liu
Dictionary learning methods can be split into: i) class specific dictionary learning ii) class shared dictionary learning.
1 code implementation • 7 Mar 2019 • Shuai Shao, Yan-Jiang Wang, Bao-Di Liu, Weifeng Liu, Rui Xu
Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification.
no code implementations • 9 Jun 2014 • Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu
A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i. e. D=UV+E.