Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets

TL;DR

This paper introduces a hierarchical subspace learning approach to enhance classification accuracy in large datasets by effectively reducing dimensionality, demonstrating improvements across multiple datasets and classifiers.

Contribution

A novel hierarchical method for subspace learning that scales to large datasets and improves classification accuracy by 3-10% over existing methods.

Findings

Achieved 3-10% improvement in classification accuracy.

Effective across multiple eigen-value based subspace methods.

Consistent accuracy gains with various classifiers.

Abstract

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.