This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide ...

Researchers at Nanjing University of Science and Technology (NJUST) developed PCA-3DSIM, a mathematically grounded ...

Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCAbased indices are constructed, how ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Using the two principal components of a point cloud for robotic grasping as an example, we will derive a numerical implementation of the PCA, which will help to understand what PCA is and what it does ...

Dimension Reduction and Classification Using PCA, Factor Analysis and Discriminant Functions - A Short Overview Course Topics Tuesday, October 28: Often researchers are faced with data in very high ...