Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
SOM and Feature Weights Based Method for Dimensionality Reduction in Large Gauss Linear Models
verfasst von : Fernando Pavón, Jesús Vega, Sebastián Dormido Canto
Erschienen in: Statistical Learning and Data Sciences
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Discovering the most important variables is a crucial step for accelerating model building without losing potential predictive power of the data. In many practical problems is necessary to discover the dependant variables and the ones that are redundant. In this paper an automatic method for discovering the most important signals or characteristics to build data-driven models is presented. This method was developed thinking in a very high dimensionality inputs spaces, where many variables are independent, but existing many others which are combinations of the independent ones. The base of the method are the SOM neural network and a method for feature weighting very similar to Linear Discriminant Analysis (LDA) with some modifications.