PRINCIPAL COMPONENT ANALYSIS AND FACTOR ANALYSIS

By G. Belle, L. Fisher, Patrick J. Heagerty, Thomas Lumley. 2004

681 citations recorded by the source corpus.

This chapter contains sections titled: Introduction, ICA and PCA, Eigenvectors and Eigenvalues, PCA Applied to Speech Signal Mixtures, Factor Analysis, Summary

Subfields: Analytical Chemistry, Statistics and Probability

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 Statistics and Probability

Citations in the corpus

Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, methodology): “The problem of separating latent factors is an old problem that has previously been attacked using a variety of techniques such as Principal Component Analysis [61], Independent Component Analysis [62], and Nonnegative Matrix Factorization [63].”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “A procedure similar to PCA but from a different theoretical perspective is Factor Analysis (FA) [99], which”
The Price of Fair PCA: One Extra Dimension (2018, methodology): “We focus on principal component analysis (henceforth PCA), perhaps the most fundamental dimensionality reduction technique in the sciences [Pearson, 1901; Hotelling, 1933; Jolliffe, 1986].”
Interpretable Basis Decomposition for Visual Explanation (2018, methodology): “Principal Component Analysis [12] and Independent Component Analysis [11] have been widely used to disentangle a lowdimensional basis from high-dimensional data.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “As embedding represents a graph in a vector space, dimensionality reduction techniques like Principal Component Analysis (PCA) [48] and t-distributed stochastic neighbor embedding (t-SNE) [8] can be applied on it to visualize the graph.”
Online Machine Learning in Big Data Streams (2018, methodology): “Principal component analysis (PCA) is a powerful tool for dimensionality reduction [128] based on matrix factorization.”
Optimizing the Latent Space of Generative Networks (2017, methodology): “Our experiments provide quantitative and qualitative comparisons to Principal Component Analysis (PCA), Deep Convolutional Auto-Encoders (DCAE), and GANs.”
Deep Anomaly Detection Using Geometric Transformations (2018, methodology): “Classic methods belonging to this category include Principal Component Analysis (PCA) [15], and Robust-PCA [4].”
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, methodology): “The problem of separating latent factors is an old problem that has previously been attacked using a variety of techniques such as Principal Component Analysis [61], Independent Component Analysis [62], and Nonnegative Matrix Factorization [63].”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, methodology): “Global methods try to preserve all-point-pair distances, which may result in more faithful projections of the high-dimensional space, but show cluster separation less well [16]; Ease of Use.”
Predictive Embeddings for Hate Speech Detection on Twitter (2018, methodology): “We then ran PCA ( ?”
Critical Barriers to Implementation of Reverse Logistics in the Manufacturing Industry: A Case Study of a Developing Country (2018, methodology): “Factor analysis is a significant and widely used technique to observe reliability of constructs [130].”
Deep Feature Factorization For Concept Discovery (2018, methodology): “Aclassical method for dimensionality reduction is principal component analysis (PCA) [18].”