[HTML][HTML] A guide for sparse pca: Model comparison and applications
PCA is a popular tool for exploring and summarizing multivariate data, especially those
consisting of many variables. PCA, however, is often not simple to interpret, as the …
consisting of many variables. PCA, however, is often not simple to interpret, as the …
[HTML][HTML] Simultaneous clustering and variable selection: A novel algorithm and model selection procedure
The growing availability of high-dimensional data sets offers behavioral scientists an
unprecedented opportunity to integrate the information hidden in the novel types of data (eg …
unprecedented opportunity to integrate the information hidden in the novel types of data (eg …
Jack of all Trades, Master of None: The Trade-offs in Sparse PCA Methods for Diverse Purposes
RG Urzola - 2023 - research.tilburguniversity.edu
Sparse algorithms are becoming increasingly popular in data science research because
they can identify and select the most relevant variables in a dataset while minimizing …
they can identify and select the most relevant variables in a dataset while minimizing …
Detecting outlying variables in multigroup data: A comparison of different loading similarity coefficients
S Gvaladze, K De Roover, F Tuerlinckx… - Journal of …, 2021 - Wiley Online Library
Multivariate multigroup data are collected in many fields of science, where the so‐called
groups pertain to, for instance, experimental groups or countries the participants are nested …
groups pertain to, for instance, experimental groups or countries the participants are nested …
[PDF][PDF] Rosember Guerra-Urzola
K Van Deun, JC Vera, K Sijtsma - 2021 - researchgate.net
PCA is a popular tool for exploring and summarizing multivariate data, especially those
consisting of many variables. PCA, however, is often not simple to interpret, as the …
consisting of many variables. PCA, however, is often not simple to interpret, as the …