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ParaDime: A Framework for Parametric Dimensionality Reduction.
Hinterreiter, Andreas; Humer, Christina; Kainz, Bernhard; Streit, Marc.
Afiliación
  • Hinterreiter A; Johannes Kepler University Linz Austria.
  • Humer C; Johannes Kepler University Linz Austria.
  • Kainz B; Friedrich-Alexander-University Erlangen-Nuremberg Germany.
  • Streit M; Imperial College London UK.
Comput Graph Forum ; 42(3): 337-348, 2023 Jun.
Article en En | MEDLINE | ID: mdl-38505300
ABSTRACT
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter-item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of the DR process. We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high-dimensional data.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Graph Forum Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Graph Forum Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido