  Unlike the field of visual scene recognition where tremendous advances have
taken place due to the availability of very large datasets to train deep neural
networks, inference from medical images is often hampered by the fact that only
small amounts of data may be available. When working with very small dataset
problems, of the order of a few hundred items of data, the power of deep
learning may still be exploited by using a model pre-trained on natural images
as a feature extractor and carrying out classic pattern recognition techniques
in this feature space, the so-called few-shot learning problem. In regimes
where the dimension of this feature space is comparable to or even larger than
the number of items of data, dimensionality reduction is a necessity and is
often achieved by principal component analysis, i.e., singular value
decomposition (SVD). In this paper, noting the inappropriateness of using SVD
for this setting, we usher in and explore two alternatives based on
discriminant analysis and non-negative matrix factorization (NMF). Using 14
different datasets spanning $11$ distinct disease types, we demonstrate that
discriminant subspaces at low dimensions achieve significant improvements over
SVD-based subspaces and the original feature space. We also show that NMF at
modest dimensions is a competitive alternative to SVD in this setting.
