Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth

Calder, Jeff; Ettehad, Mahmood

doi:10.48550/arxiv.2202.08789

Cited by 1 publication

(1 citation statement)

References 53 publications

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“…4 Several recent papers have successfully used variational autoencoders (VAEs) for unsupervised feature extraction in hyperspectral imagery, 5 SAR imagery, 6,7 and for constructing similarity graphs in graph-based learning. 8,9 VAE feature learning is an unsupervised method that retains the power and flexibility of deep supervised learning, making it ideal for problems with limited amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Graph-based Active Learning for Semi-supervised Classification of SAR Data

Miller¹,

Mauro²,

Setiadi³

et al. 2022

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We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine learning are based on a similarity graph constructed from the data. When the data consists of raw images composed of scenes, extraneous information can make the classification task more difficult. In recent years, neural network methods have been shown to provide a promising framework for extracting patterns from SAR images. These methods, however, require ample training data to avoid overfitting. At the same time, such training data are often unavailable for applications of interest, such as automatic target recognition (ATR) and SAR data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semisupervised learning techniques. The CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting and improves the generalization performance of graph learning at low label rates. Furthermore, the method easily incorporates a human-in-the-loop for active learning in the data-labeling process. We present promising results and compare them to other standard machine learning methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset for ATR with small amounts of labeled data.

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