What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Data mining, Recommender system, Mathematical optimization, Collaborative filtering and Sparse matrix are his primary areas of study.
His Data mining research integrates issues from Data modeling, Quality of service, Artificial intelligence and Machine learning, Factor analysis.
His Artificial intelligence research includes elements of Rank and Pattern recognition.
His Mathematical optimization study combines topics in areas such as Artificial neural network, Monotonic function, Hessian matrix and Affine transformation.
The Collaborative filtering study combines topics in areas such as Matrix decomposition and Scalability.
His Sparse matrix study combines topics from a wide range of disciplines, such as Non-negative matrix factorization and Theoretical computer science.
His most cited work include:
- An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems (266 citations)
- A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method (181 citations)
- Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks (179 citations)
What are the main themes of his work throughout his whole career to date?
Xin Luo mostly deals with Data mining, Recommender system, Sparse matrix, Artificial intelligence and Algorithm.
His Data mining research includes themes of Web service, Quality of service, Factor, Collaborative filtering and Series.
His research investigates the connection between Collaborative filtering and topics such as Matrix decomposition that intersect with problems in AdaBoost.
He combines subjects such as Matrix, Mathematical optimization and Missing data with his study of Recommender system.
In his study, Factor analysis is strongly linked to Data modeling, which falls under the umbrella field of Sparse matrix.
His work in Artificial intelligence covers topics such as Machine learning which are related to areas like Identification.
He most often published in these fields:
- Data mining (36.65%)
- Recommender system (32.92%)
- Sparse matrix (22.98%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (20.50%)
- Recommender system (32.92%)
- Sparse matrix (22.98%)
In recent papers he was focusing on the following fields of study:
Xin Luo spends much of his time researching Algorithm, Recommender system, Sparse matrix, Missing data and Matrix.
His research in Algorithm intersects with topics in Factorization, Process, Normalization and Cluster analysis.
The study incorporates disciplines such as Scalability, Stochastic gradient descent and Data mining in addition to Recommender system.
His biological study deals with issues like Factor, which deal with fields such as Web service.
Xin Luo interconnects Matrix decomposition, Rate of convergence, Convergence and High dimensional in the investigation of issues within Sparse matrix.
His work deals with themes such as Data modeling, Representation and Factor analysis, which intersect with Matrix.
Between 2019 and 2021, his most popular works were:
- Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors (73 citations)
- Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications (39 citations)
- Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Xin Luo mainly focuses on Algorithm, Control theory, Sparse matrix, Missing data and Data modeling.
His work carried out in the field of Control theory brings together such families of science as Artificial neural network, Redundancy and Particle swarm optimization.
Xin Luo has included themes like Matrix decomposition, Matrix and Recommender system in his Missing data study.
The various areas that Xin Luo examines in his Matrix decomposition study include Scalability and Collaborative filtering.
As part of the same scientific family, he usually focuses on Recommender system, concentrating on Rate of convergence and intersecting with Stochastic gradient descent.
His Quality of service research is multidisciplinary, incorporating elements of Process, Data mining and Service.
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