Dimensionality Reduction Ensembles
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality ReductionDistributed ComputingEnsemble LearningSimilar Papers 제목 키워드 기반
Ensembles of Classifiers based on Dimensionality Reduction
We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. Dimensionality reduction methods represent datasets using a small number of attributes while preserving the info…
Dimensionality ReductionMachine Learning for Scientific Visualization: Ensemble Data Analysis
Scientific simulations and experimental measurements produce vast amounts of spatio-temporal data, yet extracting meaningful insights remains challenging due to high dimensionality, complex structures, and missing inform…
Dimensionality ReductionLatent Diffusion Model for Generating Ensembles of Climate Simulations
Obtaining accurate estimates of uncertainty in climate scenarios often requires generating large ensembles of high-resolution climate simulations, a computationally expensive and memory intensive process. To address this…
DenoisingDimensionality ReductionUncertainty QuantificationLow Dimensional Dynamics of Globally Coupled Complex Riccati Equations: Exact Firing-rate Equations for Spiking Neurons with Clustered Substructure
We report on an exact theory for ensembles of globally coupled, heterogeneous complex Riccati equations. A drastic dimensionality reduction to a few ordinary differential equations is achieved for Lorentzian heterogeneit…
Dimensionality ReductionLearning low-dimensional representations of ensemble forecast fields using autoencoder-based methods
Large-scale numerical simulations often produce high-dimensional gridded data that is challenging to process for downstream applications. A prime example is numerical weather prediction, where atmospheric processes are m…
Dimensionality Reduction