Dimensionality Reduction Ensembles
This work addresses the problem of improving unsupervised learning for researchers and practitioners, but it is incremental as it extends ensemble techniques to dimensionality reduction.
The study tackled the lack of ensemble methods in unsupervised learning by proposing dimensionality reduction ensembles using PCA and manifold learning, achieving accuracies close to those of the full dataset on medical data.
Ensemble learning has had many successes in supervised learning, but it has been rare in unsupervised learning and dimensionality reduction. This study explores dimensionality reduction ensembles, using principal component analysis and manifold learning techniques to capture linear, nonlinear, local, and global features in the original dataset. Dimensionality reduction ensembles are tested first on simulation data and then on two real medical datasets using random forest classifiers; results suggest the efficacy of this approach, with accuracies approaching that of the full dataset. Limitations include computational cost of some algorithms with strong performance, which may be ameliorated through distributed computing and the development of more efficient versions of these algorithms.