Jingyu Yang

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Machine learning
• Statistics

Jingyu Yang spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Linear discriminant analysis and Facial recognition system. His Artificial intelligence research focuses on Computer vision and how it relates to Normalization. His research links Feature with Pattern recognition.

His research investigates the connection with Feature extraction and areas like Discriminative model which intersect with concerns in Image compression and Feature selection. He combines subjects such as Kernel Fisher discriminant analysis, Face and Biometrics with his study of Linear discriminant analysis. His Facial recognition system study combines topics from a wide range of disciplines, such as Time complexity, Representation, Contextual image classification, Invariant and Wavelet.

His most cited work include:

• Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2969 citations)
• KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (750 citations)
• Why can LDA be performed in PCA transformed space (518 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Feature extraction, Facial recognition system and Linear discriminant analysis. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. Pattern recognition connects with themes related to Feature in his study.

His biological study spans a wide range of topics, including Projection, Feature vector, Contextual image classification, Kernel principal component analysis and Dimensionality reduction. His Facial recognition system study integrates concerns from other disciplines, such as Subspace topology, Speech recognition, Kernel and Support vector machine. His Linear discriminant analysis research is multidisciplinary, incorporating perspectives in Fuzzy set, Fuzzy logic, Kernel and k-nearest neighbors algorithm.

He most often published in these fields:

• Artificial intelligence (81.22%)
• Pattern recognition (57.67%)
• Feature extraction (33.33%)

What were the highlights of his more recent work (between 2013-2021)?

• Artificial intelligence (81.22%)
• Pattern recognition (57.67%)
• Machine learning (15.61%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Data mining and Algorithm. His study ties his expertise on Computer vision together with the subject of Artificial intelligence. His study in Pattern recognition is interdisciplinary in nature, drawing from both Facial recognition system, Feature and Subspace topology.

His work on Cold start, Recommender system, Random forest and Discriminative model as part of general Machine learning study is frequently connected to Task analysis, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His research in Data mining intersects with topics in Correlation clustering, Cluster analysis and Decision rule. His Algorithm research incorporates elements of Pixel, Image, Mathematical optimization and Matrix norm.

Between 2013 and 2021, his most popular works were:

• Content-based image retrieval using computational visual attention model (137 citations)
• Updating multigranulation rough approximations with increasing of granular structures (88 citations)
• Multi-view low-rank dictionary learning for image classification (78 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Machine learning
• Statistics

Jingyu Yang mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Rough set and Algorithm. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Identification. His Pattern recognition study frequently draws connections to other fields, such as Regularization.

Jingyu Yang usually deals with Machine learning and limits it to topics linked to Constraint and Partition, Construct, Multiset and Contextual image classification. His studies in Algorithm integrate themes in fields like Nucleotide and Protein–protein interaction. His Semi-supervised learning study integrates concerns from other disciplines, such as Linear discriminant analysis and Dimensionality reduction.