Principal Component Analysis

By Ian T. Jolliffe. 2005

578 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability, Signal Processing

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “reduction refers to the method of reducing data sources’ dimensions for features extraction or visualization purposes.Examples of dimension reduction techniques are Principal Component Analysis (PCA) [75], and others [38].The aim is to preserve the characteristic of the data sources while reducing t”
Explaining anomalies detected by autoencoders using Shapley Additive Explanations (2021, methodology): “Another common approach is the visualization of clusters in a two-dimensional graph using principle component analysis (PCA) projections or t-SNE [18, 19].”
Euclidean Distance Geometry and Applications (2012, methodology): “MDS is complementary to Principal Component Analysis (PCA) [107, 85], in the following sense.”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “As such, the developers introduced fastMNN [5, 7], which applies the MNN scheme in the subspace computed using principal component analysis (PCA) [8], resulting in significant improvements in both runtime and accuracy.”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, methodology): “This technique helps the user reducing the size of variables and making the information less redundant [59].”
A compilation of UAV applications for precision agriculture (2020, unknown): “The Principal Component Analysis (PCA) [153] method was utilised to reduce the datasets’ dimensionality.”
Machine learning in medicine: a practical introduction (2019, methodology): “These techniques are often referred to as dimension reduction techniques and include processes such as principal component analysis, latent Dirichlet analysis and t-Distributed Stochastic Neighbour Embedding (t-SNE) [14–16].”