Principal Component Analysis on Spatial Data: An Overview

By Urška Demšar, Paul Harris, Chris Brunsdon, A. Stewart Fotheringham, Seán F. McLoone. 2013

453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Media Technology

How researchers see this paper

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

In Artificial Intelligence

1. Paul J. Kennedy
2. Qinxue Meng

In Economics and Econometrics

1. Liping Zhan
2. Rui Zhao
3. Linchuan Yang
4. Mingxing Yao

Citations in the corpus

• Relational autoencoder for feature extraction (2017, methodology): “The most famous projection methods are Principal Component Analysis (PCA) [10] and Linear Discriminant Analysis (LDA) [11].”
• A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2.5 (2020, methodology): “These PCs are used as input for the GWR model.”