2020 - Fellow of the Indian National Academy of Engineering (INAE)
His primary scientific interests are in Artificial intelligence, Pattern recognition, Cluster analysis, Machine learning and Data mining. Artificial intelligence is a component of his Feature, Feature selection, Feature extraction, Dimensionality reduction and Contextual image classification studies. His work deals with themes such as Regularization, Outlier and Subspace topology, which intersect with Pattern recognition.
His Cluster analysis study incorporates themes from Algorithm and Non-negative matrix factorization. His Machine learning study combines topics in areas such as Cognition, Neuroimaging and Automatic image annotation. His Data mining research incorporates elements of Data point, Feature, Feature learning and k-means clustering.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Cluster analysis. Heng Huang interconnects Data mining and Computer vision in the investigation of issues within Artificial intelligence. His study in Contextual image classification extends to Pattern recognition with its themes.
His Machine learning research integrates issues from Graph, Neuroimaging and Training set. His work carried out in the field of Algorithm brings together such families of science as Metric and Benchmark. His work in Cluster analysis covers topics such as Mathematical optimization which are related to areas like Stochastic gradient descent and Rate of convergence.
Heng Huang focuses on Artificial intelligence, Algorithm, Deep learning, Benchmark and Machine learning. In his work, Domain is strongly intertwined with Pattern recognition, which is a subfield of Artificial intelligence. His research investigates the connection between Algorithm and topics such as Kernel that intersect with problems in Kernel method, Kernel and Cluster analysis.
Heng Huang has researched Deep learning in several fields, including Eye disease, Age related, Image and Computer engineering. His research integrates issues of Discrete mathematics, Applied mathematics, Rate of convergence, Generalization and Stationary point in his study of Benchmark. His work on Labeled data and Support vector machine as part of his general Machine learning study is frequently connected to Focus, thereby bridging the divide between different branches of science.
Artificial intelligence, Algorithm, Benchmark, Segmentation and Rate of convergence are his primary areas of study. Artificial intelligence is closely attributed to Machine learning in his research. His Algorithm research integrates issues from Current, Information privacy and Feature learning.
The study incorporates disciplines such as Perspective, Estimator and Theoretical computer science in addition to Benchmark. Segmentation is a subfield of Pattern recognition that Heng Huang investigates. The concepts of his Pattern recognition study are interwoven with issues in Margin and Feature.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
Feiping Nie;Heng Huang;Xiao Cai;Chris H. Ding.
neural information processing systems (2010)
Clustering and projected clustering with adaptive neighbors
Feiping Nie;Xiaoqian Wang;Heng Huang.
knowledge discovery and data mining (2014)
Multi-view K-means clustering on big data
Xiao Cai;Feiping Nie;Heng Huang.
international joint conference on artificial intelligence (2013)
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities
Franklin L. Quilumba;Wei-Jen Lee;Heng Huang;David Yanshi Wang.
IEEE Transactions on Smart Grid (2015)
The Constrained Laplacian Rank algorithm for graph-based clustering
Feiping Nie;Xiaoqian Wang;Michael I. Jordan;Heng Huang.
national conference on artificial intelligence (2016)
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Kamran Ghasedi Dizaji;Amirhossein Herandi;Cheng Deng;Weidong Cai.
international conference on computer vision (2017)
Multi-view Subspace Clustering
Hongchang Gao;Feiping Nie;Xuelong Li;Heng Huang.
international conference on computer vision (2015)
Large-scale multi-view spectral clustering via bipartite graph
Yeqing Li;Feiping Nie;Heng Huang;Junzhou Huang.
national conference on artificial intelligence (2015)
Robust nonnegative matrix factorization using L21-norm
Deguang Kong;Chris Ding;Heng Huang.
conference on information and knowledge management (2011)
Multi-View Clustering and Feature Learning via Structured Sparsity
Hua Wang;Feiping Nie;Heng Huang.
international conference on machine learning (2013)
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