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

  • Dades identificatives

    Identificador: imarina:9378965
    Autors:
    Alvarado-Pérez JCGarcia MAPuig D
    Resum:
    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 ((Formula presented.) curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.
  • Altres:

    Autor segons l'article: Alvarado-Pérez JC; Garcia MA; Puig D
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Puig Valls, Domènec Savi
    Paraules clau: Cluster inductions Dimensionality reductions Ensemble learning Manifold approximations Online processing Topological preservations Unsupervised deep networks
    Resum: 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 ((Formula presented.) curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.
    Àrees temàtiques: Artificial intelligence Automation & control systems Computer science, artificial intelligence Computer vision and pattern recognition Control and systems engineering Electrical and electronic engineering Human-computer interaction Materials science (miscellaneous) Mechanical engineering Robotics
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-0562-4205
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://onlinelibrary.wiley.com/doi/10.1002/aisy.202400178
    Referència a l'article segons font original: Advanced Intelligent Systems.
    Referència de l'ítem segons les normes APA: Alvarado-Pérez JC; Garcia MA; Puig D (2024). Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings. Advanced Intelligent Systems, (), -. DOI: 10.1002/aisy.202400178
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1002/aisy.202400178
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2024
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Artificial Intelligence,Automation & Control Systems,Computer Science, Artificial Intelligence,Computer Vision and Pattern Recognition,Control and Systems Engineering,Electrical and Electronic Engineering,Human-Computer Interaction,Materials Science (Miscellaneous),Mechanical Engineering,Robotics
    Cluster inductions
    Dimensionality reductions
    Ensemble learning
    Manifold approximations
    Online processing
    Topological preservations
    Unsupervised deep networks
    Artificial intelligence
    Automation & control systems
    Computer science, artificial intelligence
    Computer vision and pattern recognition
    Control and systems engineering
    Electrical and electronic engineering
    Human-computer interaction
    Materials science (miscellaneous)
    Mechanical engineering
    Robotics
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