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 ...

The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, is simplicity -- assuming you have a function that computes eigenvalues and ...

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 ...

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 ...

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

Benjamin Eltzner, Stephan Huckemann, Kanti V. Mardia, TORUS PRINCIPAL COMPONENT ANALYSIS WITH APPLICATIONS TO RNA STRUCTURE, The Annals of Applied Statistics, Vol. 12, No. 2 (June 2018), pp. 1332-1359 ...