Principal component analysis

2,585 citations recorded by the source corpus.

How researchers see this paper

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

Text Classification Algorithms: A Survey (2019, positive): “PCA is a method to identify a subspace in which the data approximately lies [68].”
High‐dimensional covariance matrix estimation (2019, neutral): “For a general PCA review, see Abdi and Williams (2010) and Johnstone and Paul (2018) and the references therein.”
Credit Card Fraud Detection Using State-of-the-art Machine Learning and Deep Learning Algorithms (2023, neutral): “All these weights were adjusted by detailed algorithms such as backpropagation [2], [6].”
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, positive): “For example, PCA converts a set of correlated variables into a reduced set of uncorrelated new variables or principal components (PCs) using orthogonal transformation [40].”
Next-Generation Machine Learning for Biological Networks. (2018, positive): “As an example, by plotting the first two principal components of a PCA, one can judge the relative distance (a metric of similarity) between samples on a simple two-dimensional plot summarizing information from hundreds or thousands of features (Abdi and Williams, 2010; Shlens, 2014).”