What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
Ping Li spends much of his time researching Artificial intelligence, Theoretical computer science, Locality-sensitive hashing, Set and K-independent hashing.
His Artificial intelligence research includes elements of Probability and statistics, Machine learning and Pattern recognition.
His research in the fields of Learning to rank, Discounted cumulative gain, Support vector machine and Sentiment analysis overlaps with other disciplines such as Gradient boosting.
The study incorporates disciplines such as Universal hashing and Dynamic perfect hashing in addition to K-independent hashing.
His biological study deals with issues like Key, which deal with fields such as Algorithm.
As a part of the same scientific study, he usually deals with the Computation, concentrating on Discrete mathematics and frequently concerns with Combinatorics.
His most cited work include:
- Very sparse random projections (466 citations)
- McRank: Learning to Rank Using Multiple Classification and Gradient Boosting (338 citations)
- User-level sentiment analysis incorporating social networks (312 citations)
What are the main themes of his work throughout his whole career to date?
Ping Li mainly focuses on Algorithm, Artificial intelligence, Pattern recognition, Theoretical computer science and Data mining.
In his research on the topic of Algorithm, Rank, Dimension and Time complexity is strongly related with Matrix norm.
Within one scientific family, Ping Li focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Benchmark.
His Data mining research integrates issues from Sketch and Contingency table.
His research investigates the connection between Sketch and topics such as Sampling that intersect with issues in Sparse matrix and Pairwise comparison.
His research in Dynamic perfect hashing intersects with topics in Linear hashing, 2-choice hashing and Universal hashing.
He most often published in these fields:
- Algorithm (34.16%)
- Artificial intelligence (24.22%)
- Pattern recognition (15.53%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (24.22%)
- Pattern recognition (15.53%)
- Algorithm (34.16%)
In recent papers he was focusing on the following fields of study:
Ping Li mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Applied mathematics and Combinatorics.
The concepts of his Artificial intelligence study are interwoven with issues in Matrix decomposition and Machine learning.
In his work, Cluster analysis, Ranking SVM, Ranking and Pairwise comparison is strongly intertwined with Data mining, which is a subfield of Machine learning.
His Pattern recognition study integrates concerns from other disciplines, such as Coherence, Graph classification and Linear regression.
His Compressed sensing, Signal recovery, Decoding methods and Non linear estimation study in the realm of Algorithm connects with subjects such as Bit.
His work carried out in the field of Applied mathematics brings together such families of science as Mathematical optimization and Consistency.
Between 2015 and 2021, his most popular works were:
- Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit (60 citations)
- Recovery of sparse signals via generalized orthogonal matching pursuit: A new analysis (54 citations)
- Gradient Hard Thresholding Pursuit (45 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary scientific interests are in Thresholding, Artificial intelligence, Pattern recognition, Matrix and Restricted isometry property.
His Artificial intelligence research is multidisciplinary, incorporating elements of Ranking, Machine learning, Categorical variable and Pointwise.
His work focuses on many connections between Pattern recognition and other disciplines, such as Robustness, that overlap with his field of interest in Subspace topology.
His work in Matrix tackles topics such as Constant which are related to areas like Combinatorics, Algorithm design, Signal-to-noise ratio and Relaxation.
Ping Li frequently studies issues relating to Algorithm and Connection.
The Algorithm study combines topics in areas such as Linear subspace and Matrix norm.
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