  Manifold learning is used for dimensionality reduction, with the goal of
finding a projection subspace to increase and decrease the inter- and
intraclass variances, respectively. However, a bottleneck for subspace learning
methods often arises from the high dimensionality of datasets. In this paper, a
hierarchical approach is proposed to scale subspace learning methods, with the
goal of improving classification in large datasets by a range of 3% to 10%.
Different combinations of methods are studied. We assess the proposed method on
five publicly available large datasets, for different eigen-value based
subspace learning methods such as linear discriminant analysis, principal
component analysis, generalized discriminant analysis, and reconstruction
independent component analysis. To further examine the effect of the proposed
method on various classification methods, we fed the generated result to linear
discriminant analysis, quadratic linear analysis, k-nearest neighbor, and
random forest classifiers. The resulting classification accuracies are compared
to show the effectiveness of the hierarchical approach, reporting results of an
average of 5% increase in classification accuracy.
