What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer network
Shengli Xie mainly focuses on Smart grid, Computer network, Blind signal separation, Matrix decomposition and Algorithm.
His work deals with themes such as Distributed algorithm, Distributed computing, Energy management, Energy consumption and Optimization problem, which intersect with Smart grid.
The study of Computer network is intertwined with the study of Cognitive radio in a number of ways.
He interconnects Data mining, Multiset, Matrix, Sparse matrix and Component analysis in the investigation of issues within Blind signal separation.
The study incorporates disciplines such as Generalization and Feature extraction in addition to Matrix decomposition.
His study in Algorithm is interdisciplinary in nature, drawing from both Evolutionary computation, Probabilistic logic and Cluster analysis.
His most cited work include:
- Home M2M networks: Architectures, standards, and QoS improvement (372 citations)
- Cognitive machine-to-machine communications: visions and potentials for the smart grid (333 citations)
- Underdetermined blind source separation based on sparse representation (292 citations)
What are the main themes of his work throughout his whole career to date?
Shengli Xie mostly deals with Algorithm, Artificial intelligence, Cluster analysis, Blind signal separation and Matrix.
His research in Algorithm is mostly focused on Sparse approximation.
His Sparse approximation study combines topics from a wide range of disciplines, such as Underdetermined system and Sparse matrix.
Shengli Xie has researched Artificial intelligence in several fields, including Machine learning, Computer vision and Pattern recognition.
The Cluster analysis study combines topics in areas such as Graph, Subspace topology, Non-negative matrix factorization and Graph.
His work is dedicated to discovering how Blind signal separation, Matrix decomposition are connected with Computational complexity theory and other disciplines.
He most often published in these fields:
- Algorithm (27.02%)
- Artificial intelligence (21.05%)
- Cluster analysis (13.33%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (27.02%)
- Cluster analysis (13.33%)
- Matrix (12.98%)
In recent papers he was focusing on the following fields of study:
Shengli Xie focuses on Algorithm, Cluster analysis, Matrix, Artificial intelligence and Matrix decomposition.
A large part of his Algorithm studies is devoted to Optimization problem.
His Cluster analysis research includes elements of Graph, Feature, Representation, Sparse matrix and Graph.
His Matrix research integrates issues from Time domain, Structure, Blind signal separation, Independent component analysis and Frequency domain.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His research in Matrix decomposition is mostly concerned with Non-negative matrix factorization.
Between 2019 and 2021, his most popular works were:
- Joint Transaction Relaying and Block Verification Optimization for Blockchain Empowered D2D Communication (13 citations)
- Non-Negative Matrix Factorization With Dual Constraints for Image Clustering (12 citations)
- Deep graph regularized non-negative matrix factorization for multi-view clustering (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer network
Shengli Xie spends much of his time researching Cluster analysis, Algorithm, Matrix, Matrix decomposition and Non-negative matrix factorization.
His biological study deals with issues like Graph, which deal with fields such as Graph, Tucker decomposition, Rank and Spectral gap.
His research in Algorithm intersects with topics in Representation, Sparse matrix, Unsupervised learning, Fourier transform and Frequency domain.
His studies in Matrix integrate themes in fields like Time domain, Optimization problem, Independent component analysis and Blind signal separation.
His research investigates the connection with Matrix decomposition and areas like Computational complexity theory which intersect with concerns in Dimension, Trace, Embedding and Lemma.
His work carried out in the field of Artificial intelligence brings together such families of science as Laplacian matrix and Rank.
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