Dimensionality Reduction via Community Detection in Small Sample Datasets

Abstract

Real world networks constructed from raw data are often characterized by complex community structures. Existing dimensionality reduction techniques, however, do not take such characteristics into account. This is especially important for problems with low number of samples where the curse of dimensionality is particularly significant. Therefore, in this paper, we propose FeatureNet, a novel community-based dimensionality reduction framework targeting small sample problems. To this end, we propose a new method to directly construct a network from high-dimensional raw data while explicitly revealing its hidden community structure; these communities are then used to learn low-dimensional features using a representation learning framework. We show the effectiveness of our approach on eight datasets covering application areas as diverse as handwritten digits, biology, physical sciences, NLP, and computational sustainability. Extensive experiments on the above datasets (with sizes mostly between 100 and 1500 samples) demonstrate that FeatureNet significantly outperforms (i.e., up to 40% improvement in classification accuracy) ten well-known dimensionality reduction methods like PCA, Kernel PCA, Isomap, SNE, t-SNE, etc.