Dimensionality reduction with missing values imputation

In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous data that limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation's method. The main purpose here is to extract us...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Gahar, Rania Mkhinini, Arfaoui, Olfa, Hidri, Minyar Sassi, Alouane, Nejib Ben-Hadj
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Data Structures and Algorithms Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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