Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification

Abstract

Recently, graph embedding-based methods have been developed in dimensionality reduction (DR) and classification of hyperspectral image (HSI). The key step for graph embedding methods is the construction of graph. The commonly used method is to manually choose nearest neighbors and then, compute edge weights using the spectral feature. However, the adjacency graph is inappropriate due to the negligence of spatial information. What is more, the construction of graph only takes training samples or k nearest neighbors into account, which may lead to unsuitable graph representation. In this paper, we propose a novel incremental graph embedding (IGE) algorithm to construct a spatial-spectral neighbor graph for the HSI classification. The IGE can spread the discrimination information contained in training samples to their neighbors until each testing sample has a pseudo label. The spatial affinity weights between unlabeled data points and their labeled neighbors are calculated according to the construction strategies of the spatialspectral neighbor graph. The pseudo label of the unlabeled data point is determined based on the maximum spatial affinity weights. Moreover, three weight strategies are designed for those samples nearby the decision boundary to improve the separability of different classes. In addition, the window size of spatial neighbors is able to be adjusted adaptively according to whether labeled data points in spatial neighbors exit. Experimental results on two datasets, Indian Pine and Pavia University have demonstrated that our algorithm is remarkably superior to other conventional DR algorithms on improving classification performance.

Molecular Property Prediction Based on a Multichannel Substructure Graph

“…After obtaining each feature of each substructure, the molecule-level feature is extracted. Most algorithms focus on extracting the features from the whole structure of the molecule [8], [10], [12]. However, a molecule is a special graph in which different substructures interact with each other.…”

“…Compared with fingerprints and SMILES, the graph representation could retain the molecular structure and topological information [5]. Graphs have been applied in many areas and achieved good results [11], [12]. Through the graph representation, one molecule is interpreted as an undirected graph in which an atom is regarded as a node and a bond is regarded as an edge.…”