What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Cluster analysis and Visualization.
His work on Feature learning and Automatic image annotation as part of general Artificial intelligence research is frequently linked to Matrix decomposition, 2-choice hashing and Dynamic perfect hashing, bridging the gap between disciplines.
His Pattern recognition research integrates issues from Optimization problem and Robustness.
His work carried out in the field of Machine learning brings together such families of science as Image retrieval and Semantic gap.
Zechao Li combines subjects such as Data mining and Feature selection with his study of Cluster analysis.
His Visualization research incorporates elements of Image and Image restoration.
His most cited work include:
- Unsupervised feature selection using nonnegative spectral analysis (315 citations)
- Robust Structured Subspace Learning for Data Representation (259 citations)
- Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (224 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Image retrieval.
His research ties Computer vision and Artificial intelligence together.
Zechao Li has researched Pattern recognition in several fields, including Visualization and Robustness.
Zechao Li has included themes like Metric and Semantic gap in his Machine learning study.
His Discriminative model study combines topics in areas such as Contextual image classification and Quantization.
His work in Cluster analysis covers topics such as Data mining which are related to areas like Probabilistic latent semantic analysis.
He most often published in these fields:
- Artificial intelligence (70.08%)
- Pattern recognition (47.24%)
- Machine learning (22.05%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.08%)
- Pattern recognition (47.24%)
- Machine learning (22.05%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Feature learning and Discriminative model.
His study looks at the relationship between Artificial intelligence and fields such as Computer vision, as well as how they intersect with chemical problems.
His Pattern recognition course of study focuses on Robustness and Data-driven.
The concepts of his Machine learning study are interwoven with issues in Question answering and Causal graph.
His work deals with themes such as Regularization, Inference and k-nearest neighbors algorithm, which intersect with Feature learning.
His Discriminative model research includes themes of Contextual image classification, Embedding, Data mining and Image retrieval.
Between 2019 and 2021, his most popular works were:
- Weakly-supervised Semantic Guided Hashing for Social Image Retrieval (15 citations)
- Learning Robust Discriminant Subspace Based on Joint L2,p- and L2,s-Norm Distance Metrics. (7 citations)
- FACE SUPER-RESOLUTION GUIDED BY 3D FACIAL PRIORS (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Zechao Li spends much of his time researching Artificial intelligence, Discriminative model, Computer vision, Encoder and Convolutional neural network.
The various areas that Zechao Li examines in his Artificial intelligence study include Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Contextual image classification, Iterative method, Norm and Similarity.
His Discriminative model study integrates concerns from other disciplines, such as Feature learning, Data mining and k-nearest neighbors algorithm.
His work in the fields of Computer vision, such as Segmentation and Feature, intersects with other areas such as Process and Retinal.
His studies deal with areas such as Facial expression, Face and Rendering as well as Convolutional neural network.
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