Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

Identificador:  imarina:9443785

Handle: https://hdl.handle.net/20.500.11797/imarina9443785

Autores:  Alvarado-Perez, Juan Carlos; Garcia, Miguel Angel; Puig, Domenec

Resumen:
Dimension reduction aims to project a high-dimensional dataset into a low-dimensional space. It tries to preserve the topological relationships among the original data points and/or induce clusters. NetDRm, an online dimensionality reduction method based on neural ensemble learning that integrates different dimension reduction methods in a synergistic way, is introduced. NetDRm is designed for datasets of multidimensional points that can be either structured (e.g., images) or unstructured (e.g., point clouds, tabular data). It starts by training a collection of deep residual encoders that learn the embeddings induced by multiple dimension reduction methods applied to the input dataset. Subsequently, a dense neural network integrates the generated encoders by emphasizing topological preservation or cluster induction. Experiments conducted on widely used multidimensional datasets (point-cloud manifolds, image datasets, tabular record datasets) show that the proposed method yields better results in terms of topological preservation (RNX$R_{\text{NX}}$ curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods. Dimension reduction maps high-dimensional data into low-dimensional space, preserving topological relationships and inducing clusters. NetDRm is an online method based on neural ensemble learning that integrates various reduction methods. It uses deep encoders and a dense neural network to improve topological preservation, cluster induction, and classification accuracy, surpassing relevant methods.image (c) 2024 WILEY-VCH GmbH