S
Shengli Xie
Researcher at Guangdong University of Technology
Publications - 344
Citations - 12728
Shengli Xie is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Computer science & Blind signal separation. The author has an hindex of 52, co-authored 298 publications receiving 9021 citations. Previous affiliations of Shengli Xie include South China University of Technology.
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Transferable Linear Discriminant Analysis
TL;DR: This brief proposes a novel transferable LDA (TLDA) method to extend LDA into the scenario in which the data have different probability distributions, and demonstrates that the TLDA can achieve better classification performance and outperform the state-of-the-art methods.
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Robust Spectral Subspace Clustering Based on Least Square Regression
TL;DR: A novel subspace clustering via learning an adaptive graph affinity matrix is proposed, where the soft label and the representation coefficients of data are learned in an unified framework, and demonstrates that the proposed method performs better against the state-of-the-art approaches, in clustering.
Journal ArticleDOI
Editorial A Successful Change From TNN to TNNLS and a Very Successful Year
Derong Liu,Charles W. Anderson,Ahmad Taher Azar,Giorgio Battistelli,Eduardo Bayro-Corrochano,Cristiano Cervellera,David Elizondo,Maurizio Filippone,Giorgio Gnecco,Xiaolin Hu,Tingwen Huang,Weifeng Liu,Wenlian Lu,Ana Madureira,Igor Škrjanc,Thomas Villmann,Jonathan Wu,Shengli Xie,Dong Xu +18 more
TL;DR: This issue marks the first anniversary issue of IEEE TRANSACTIONS ON NEURAL NETWORKS and LEARNING SYSTEMS after it changed its name from IEEE TransACTIONS on NEURal Networks and Learning Systems after it had a great year.
Posted Content
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning.
TL;DR: Both of the proposed models extend TR decomposition and can be served as powerful representation learning tools for non-negative multiway data.
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Nonorthogonal Approximate Joint Diagonalization With Well-Conditioned Diagonalizers
TL;DR: The approximate joint diagonalization problem is reviewed as a multiobjective optimization problem for the first time and a new algorithm for nonorthogonal joint diagonalizing is developed, which yields diagonalizers which not only minimize the diagonalization error but also have as small condition numbers as possible.