2 citations · 2 across the 3 of their papers we have counts for
5 papers
A Latent Logistic Regression Model with Graph Data
Haixiang Zhang, Yingjun Deng, Alan J. X. Guo +2
Recently, graph (network) data is an emerging research area in artificial intelligence, machine learning and statistics. In this work, we are interested in whether node's labels (p…
Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
Alan J. X. Guo, Qing-Hu Hou, Ou Wu
In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data. Most GNNs follow a message passing scheme, and their expressive pow…
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing
Alan J. X. Guo, Fei Zhu
Recent advances in neural networks have made great progress in the hyperspectral image (HSI) classification. However, the overfitting effect, which is mainly caused by complicated…
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field
Yi Liang, Xin Zhao, Alan J. X. Guo +1
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectr…
Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery
Alan J. X. Guo, Fei Zhu
In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With…