What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Computer network.
His study in Image processing, Image retrieval, Artificial neural network, Evolutionary computation and Image restoration is carried out as part of his studies in Artificial intelligence.
His Pattern recognition study frequently links to other fields, such as Facial expression.
His study in the field of Biometrics, Median filter and Segmentation is also linked to topics like Boundary.
His Feature extraction research is multidisciplinary, incorporating perspectives in Speech recognition, Facial recognition system, Wavelet transform, Discriminative model and Feature selection.
The Resource allocation, Cache and Dynamic network analysis research Ling Guan does as part of his general Computer network study is frequently linked to other disciplines of science, such as Popularity, therefore creating a link between diverse domains of science.
His most cited work include:
- Optimal Scheduling for Charging and Discharging of Electric Vehicles (506 citations)
- A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films (308 citations)
- Recognizing Human Emotional State From Audiovisual Signals (177 citations)
What are the main themes of his work throughout his whole career to date?
Ling Guan mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Artificial neural network.
Ling Guan regularly links together related areas like Machine learning in his Artificial intelligence studies.
His biological study spans a wide range of topics, including Facial recognition system, Image and Feature.
Computer vision is closely attributed to Adaptive filter in his work.
Ling Guan interconnects Contextual image classification, Speech recognition, Wavelet transform and Pattern recognition in the investigation of issues within Feature extraction.
His work on Information retrieval expands to the thematically related Image retrieval.
He most often published in these fields:
- Artificial intelligence (65.14%)
- Pattern recognition (37.61%)
- Computer vision (31.65%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (65.14%)
- Pattern recognition (37.61%)
- Discriminative model (8.03%)
In recent papers he was focusing on the following fields of study:
Ling Guan focuses on Artificial intelligence, Pattern recognition, Discriminative model, Feature extraction and Canonical correlation.
His Artificial intelligence research incorporates themes from Machine learning and Computer vision.
His work deals with themes such as Image and Representation, which intersect with Pattern recognition.
The concepts of his Discriminative model study are interwoven with issues in Quadtree, Speech recognition, Coding and Cognitive neuroscience of visual object recognition.
His study in the field of Handwriting recognition also crosses realms of Set.
His Canonical correlation research incorporates elements of Information fusion, Facial recognition system, Face and Data set.
Between 2015 and 2021, his most popular works were:
- Discriminative Multiple Canonical Correlation Analysis for Information Fusion (34 citations)
- Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis (22 citations)
- Forecasting day-ahead price spikes for the Ontario electricity market (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Ling Guan mostly deals with Artificial intelligence, Pattern recognition, Discriminative model, Canonical correlation and Feature extraction.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Graph and Computer vision.
His work on Cave automatic virtual environment as part of general Computer vision research is frequently linked to Action recognition and Cave, bridging the gap between disciplines.
His work on Neural coding as part of general Pattern recognition study is frequently linked to Locality, therefore connecting diverse disciplines of science.
His study explores the link between Discriminative model and topics such as Speech recognition that cross with problems in Multiview learning and Information fusion.
His work investigates the relationship between Feature extraction and topics such as Representation that intersect with problems in Motion, Feature, Sample and Depth map.
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