What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Yilong Yin spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Finger vein recognition and Biometrics.
His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning.
His work in the fields of Fingerprint recognition, Scale-space segmentation, Speckle noise and Grayscale overlaps with other areas such as Discrete wavelet transform.
His Pattern recognition research incorporates themes from Artificial neural network, Speech recognition and Minutiae.
He combines subjects such as Nonlinear dimensionality reduction and Database, Identification with his study of Biometrics.
His Feature extraction research is multidisciplinary, incorporating elements of Robustness and Rotation.
His most cited work include:
- On the Class Imbalance Problem (225 citations)
- Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. (199 citations)
- SDUMLA-HMT: a multimodal biometric database (188 citations)
What are the main themes of his work throughout his whole career to date?
Yilong Yin mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Biometrics.
His research on Artificial intelligence often connects related topics like Machine learning.
The Pattern recognition study combines topics in areas such as Binary code, Image, Image retrieval and Feature.
His work on Computer vision is being expanded to include thematically relevant topics such as Robustness.
His studies deal with areas such as Feature, Binary pattern, Vein, Matching and Histogram as well as Finger vein recognition.
His Convolutional neural network study combines topics in areas such as Artificial neural network and Deep learning.
He most often published in these fields:
- Artificial intelligence (82.55%)
- Pattern recognition (48.66%)
- Computer vision (27.52%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.55%)
- Pattern recognition (48.66%)
- Machine learning (12.08%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Discriminative model.
His work in Convolutional neural network, Image, Segmentation, Benchmark and Feature extraction is related to Artificial intelligence.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Embedding, Feature and Biometrics.
His Machine learning research is multidisciplinary, relying on both Object, Inference and Robustness.
Yilong Yin has researched Deep learning in several fields, including Variation, Data mining, Narrative, Field and Convolution.
While the research belongs to areas of Discriminative model, Yilong Yin spends his time largely on the problem of Linear discriminant analysis, intersecting his research to questions surrounding Cluster analysis, Codebook, Subspace topology, Pattern recognition and Finger vein recognition.
Between 2019 and 2021, his most popular works were:
- Joint Multi-View Hashing for Large-Scale Near-Duplicate Video Retrieval (9 citations)
- Learning to Learn Kernels with Variational Random Features (8 citations)
- Multi-scale differential feature for ECG biometrics with collective matrix factorization (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Biometrics.
Yilong Yin undertakes multidisciplinary investigations into Artificial intelligence and Modal in his work.
His research in the fields of Discriminative model and Pairwise similarity overlaps with other disciplines such as Intermediate language and Visual perception.
Yilong Yin has included themes like Narrative, Object detection, Field, Sensory cue and Pattern recognition in his Deep learning study.
Yilong Yin works mostly in the field of Biometrics, limiting it down to topics relating to Sparse approximation and, in certain cases, Regularization, Consistency, Discriminant, Optimization problem and Joint.
His research integrates issues of Ground truth, Kernel, Correlation coefficient and Support vector machine in his study of Regression.
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