Dimensionality Reduction Ensembles

by   Colleen M. Farrelly, et al.

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.


page 8

page 10


Foundations of Coupled Nonlinear Dimensionality Reduction

In this paper we introduce and analyze the learning scenario of coupled ...

Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction

Exploiting capacity of sewer system using decentralized control is a cos...

KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods

Very few K-nearest-neighbor (KNN) ensembles exist, despite the efficacy ...

Dimensionality reduction to maximize prediction generalization capability

This work develops an analytically solvable unsupervised learning scheme...

DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction

This paper discusses the critical decision process of extracting or sele...

A Category Space Approach to Supervised Dimensionality Reduction

Supervised dimensionality reduction has emerged as an important theme in...

Please sign up or login with your details

Forgot password? Click here to reset