scholarly journals Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification

2019 ◽  
Vol 5 (2) ◽  
pp. 148-165 ◽  
Author(s):  
Zhao Zhang ◽  
Lei Jia ◽  
Mingbo Zhao ◽  
Guangcan Liu ◽  
Meng Wang ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Label Propagation

scholarly journals Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering

2018 ◽  
Vol 10 (4) ◽  
pp. 515 ◽  
Author(s):  
Binge Cui ◽  
Xiaoyun Xie ◽  
Siyuan Hao ◽  
Jiandi Cui ◽  
Yan Lu
Keyword(s):  
Supervised Classification ◽  
Label Propagation ◽  
Hyperspectral Images

scholarly journals COUPLE GRAPH BASED LABEL PROPAGATION METHOD FOR HYPERSPECTRAL REMOTE SENSING DATA CLASSIFICATION

2018 ◽  
Vol XLII-3 ◽  
pp. 1795-1798
Author(s):  
X. P. Wang ◽  
Y. Hu ◽  
J. Chen
Keyword(s):  
Manifold Learning ◽  
Classification Accuracy ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Remote Sensing Data ◽  
Graph Laplacian ◽  
Hyperspectral Data ◽  
Label Propagation ◽  
Hyperspectral Image Classification ◽  
Adjacency Graph

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

scholarly journals Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

2021 ◽  
Author(s):  
Yunsheng Shi ◽  
Zhengjie Huang ◽  
Shikun Feng ◽  
Hui Zhong ◽  
Wenjing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Supervised Classification ◽  
State Of The Art ◽  
Label Propagation ◽  
Superior Performance ◽  
Label Prediction ◽  
Label Information ◽  
Message Passing Algorithms ◽  
Prediction Strategy

Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification. GNN performs feature propagation by a neural network to make predictions, while LPA uses label propagation across graph adjacency matrix to get results. However, there is still no effective way to directly combine these two kinds of algorithms. To address this issue, we propose a novel Unified Message Passaging Model (UniMP) that can incorporate feature and label propagation at both training and inference time. First, UniMP adopts a Graph Transformer network, taking feature embedding and label embedding as input information for propagation. Second, to train the network without overfitting in self-loop input label information, UniMP introduces a masked label prediction strategy, in which some percentage of input label information are masked at random, and then predicted. UniMP conceptually unifies feature propagation and label propagation and is empirically powerful. It obtains new state-of-the-art semi-supervised classification results in Open Graph Benchmark (OGB).

scholarly journals Masked Graph Convolutional Network

2019 ◽  
Author(s):  
Liang Yang ◽  
Fan Wu ◽  
Yingkui Wang ◽  
Junhua Gu ◽  
Yuanfang Guo
Keyword(s):  
Supervised Classification ◽  
Label Propagation ◽  
Unstructured Data ◽  
Classification Methods ◽  
Convolutional Network ◽  
Node Attributes ◽  
Classification Tasks ◽  
Node Classification ◽  
Supervised Classification Methods ◽  
The Impact

Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph which is usually constructed from the data features, while the graph convolutional neural networks smooth the node attributes, i.e., propagate the attributes, over the real graph topology. In this paper, they are interpreted from the perspective of propagation, and accordingly categorized into symmetric and asymmetric propagation based methods. From the perspective of propagation, both the traditional and network based methods are propagating certain objects over the graph. However, different from the label propagation, the intuition ``the connected data samples tend to be similar in terms of the attributes", in attribute propagation is only partially valid. Therefore, a masked graph convolution network (Masked GCN) is proposed by only propagating a certain portion of the attributes to the neighbours according to a masking indicator, which is learned for each node by jointly considering the attribute distributions in local neighbourhoods and the impact on the classification results. Extensive experiments on transductive and inductive node classification tasks have demonstrated the superiority of the proposed method.

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