Feature Selection Using Principal Component Analysis

By Fengxi Song, Zhongwei Guo, Dayong Mei. Joint Conference on Lexical and Computational Semantics, 2010

360 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cb52ddc16ec68b077ad621953dc7bf596bf6a773

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Hugo J. W. L. Aerts
2. Philippe Lambin

In Computer Vision and Pattern Recognition

1. Hugo J. W. L. Aerts
2. Philippe Lambin
3. Dmitry Goldgof

Citations in the corpus

• Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, background): “Principal component analysis performs a transformation for dimensionality reduction and can highlight outliers.(72,83) Aerts et al(61) have used the single most informative of the four Review article: Radiomics: a review on technology and methodological procedures BJR”
• QIN “Radiomics: The Process and the Challenges” (2012, methodology): “Dimensionality reduction can also be achieved by combining or transforming the original features to obtain a new set of features by using methods like principal component analysis (PCA) [73].”