Abstract:
We provide a comparative study of several state-of-the-art Mahalanobis metric learning algorithms evaluated on three well-studied, high-dimensional hyperspectral images c...Show MoreMetadata
Abstract:
We provide a comparative study of several state-of-the-art Mahalanobis metric learning algorithms evaluated on three well-studied, high-dimensional hyperspectral images captured by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument. We focus on the problem of low-rankMahalanobis metric learning, where our objective is to learn an {\mbi{n}}\ {\mmb \times}\ {\mbi{m}} projection matrix {\bf A} , where {\mbi{m}}\ {\mmb \ll}\ {\mbi{n}} . Low-rank metrics offer a “plug-in” enhancement to similarity-based classifiers that can reduce computation time and improve classification accuracy with fewer training samples, enabling operations in resource-constrained environments such as onboard spacecraft. Our results indicate that applying a simple shrinkage-based regularization procedure to multiclass Linear Discriminant Analysis (LDA) produces comparable or better classification accuracies than the low-rank extensions of several widely used Mahalanobis metric learning algorithms, at considerably lower computational cost.
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 7, Issue: 4, April 2014)