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Chang Tang
Researcher at China University of Geosciences (Wuhan)
Publications - 70
Citations - 3514
Chang Tang is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Cluster analysis & Graph (abstract data type). The author has an hindex of 25, co-authored 67 publications receiving 2034 citations. Previous affiliations of Chang Tang include Information Technology University & Tianjin University.
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Journal ArticleDOI
A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection
TL;DR: A novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions and can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region.
Journal ArticleDOI
A network traffic forecasting method based on SA optimized ARIMA–BP neural network
TL;DR: A network traffic prediction method based on SA (Simulated Annealing) optimized ARIMA, non-linear model BPNN and optimization algorithm SA can fully realize the potential of mining linear and non- linear laws of historical network traffic data, hence improving the prediction accuracy.
Journal ArticleDOI
Unsupervised feature selection via latent representation learning and manifold regularization.
TL;DR: A robust unsupervised feature selection method which embeds the latent representation learning into feature selection and is carried out in the learned latent representation space which is more robust to noises.
Proceedings ArticleDOI
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks
TL;DR: In this article, the authors proposed three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images(DDNI), and Dynamic Depth Motion Normal Images (DMNI), which are constructed from a sequence of depth maps using bidirectional rank pooling.
Journal ArticleDOI
Consensus Graph Learning for Multi-view Clustering
TL;DR: A novel multi-view clustering method that is able to construct an essential similarity graph in a spectral embedding space instead of the original feature space is proposed and an efficient optimization algorithm is designed to solve the resultant optimization problem.