Principal component analysis: a review and recent developments

By Ian T. Jolliffe, J. Cadima. 2016

9,010 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology, Signal Processing

How researchers see this paper

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

In Analytical Chemistry

In Media Technology

In Signal Processing

Citations in the corpus

Training Networks in Null Space of Feature Covariance for Continual Learning (2021, positive): “Furthermore, to empirically verify the rationality of the approximation, we utilize the proportion R of X̄ l t−1 explained by U l 2 [18] as”
Text Classification Algorithms: A Survey (2019, background): “This means finding new variables that are uncorrelated and maximizing the variance to "preserve as much variability as possible"[69].”
Selecting critical features for data classification based on machine learning methods (2020, methodology): “…(CNN) for classification tasks, such as applications in bioinformatics neurodegenerative disorders classification using the Principal Components Analysis (PCA) algorithm [112, 113], brain tumor segmentation [114] using three planar super pixel based statistical and textural features extraction.”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, methodology): “PCA does not often appear as a separate method, as it is often combined with other statistical methods [95].”
Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange (2019, methodology): “…the composite sentiment index because it allowed for the reduction of the dimensionality of the dataset, which helped in increasing interpretability without information loss in the determination of whether sentiment influences volatility on the South African market (Jolliffe & Cadima, 2016).”
Predicting academic performance of students from VLE big data using deep learning models (2020, methodology): “In terms of centered data SVD corresponds to Principle Component Analysis (PCA), which can be defined as reducing the estimation error (Jolliffe & Cadima, 2016).”