A graph Laplacian based approach to semi-supervised feature selection for regression problems

Gauthier Doquire; Michel Verleysen

doi:10.1016/j.neucom.2012.10.028

ScienceGate Book Chapters

JOURNAL ARTICLE

A graph Laplacian based approach to semi-supervised feature selection for regression problems

Gauthier Doquire Michel Verleysen

Year: 2013 Journal: Neurocomputing Vol: 121 Pages: 5-13 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2012.10.028

Get Full-Text PDF Get Analytical Report

Keywords:

Feature selection Computer science Artificial intelligence Machine learning Supervised learning Graph Regression Selection (genetic algorithm) Feature (linguistics) Pattern recognition (psychology) Semi-supervised learning Laplacian matrix Data mining Mathematics Artificial neural network Theoretical computer science Statistics

Metrics

Cited By

3.64

FWCI (Field Weighted Citation Impact)

Refs

0.95

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Fuzzy Logic and Control Systems

Physical Sciences → Computer Science → Artificial Intelligence

A graph Laplacian based approach to semi-supervised feature selection for regression problems

Metrics

Citation History

Topics

Related Documents

Graph Laplacian for Semi-supervised Feature Selection in Regression Problems

Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems

Label Reconstruction based Laplacian Score for semi-supervised feature selection

Constrained Laplacian Score for Semi-supervised Feature Selection

Clustering-Based Feature Selection in Semi-supervised Problems