A tutorial on Principal Components Analysis

By Lindsay Smith. 2002

2,102 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/462bf829634e3ffaef794de5e58809994d30f8ec

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Citations in the corpus

Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination–A review (2018, methodology): “Presentation of data by PCA aims to highlight their similarities and differences (Smith 2002).”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “, the Principal Components (PC) of the data [67] 3) randomly select 3 distinct trials XA, XB and XC 4) project the first two on the PC, i.”
When to use what: Methods for weighting and aggregating sustainability indicators (2017, unknown): “Factors that account for the largest proportion of the variance are retained, while less informative factors are ignored (Smith, 2002).”
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security (2017, unknown): “The PCA algorithm preserves two of the most informative dimensions by transforming the original coordinate system, and then a binary SVM is trained on the transformed data set.”
Face Photo-Sketch Synthesis and Recognition (2012, background): “Facial recognition is a form of computer vision which uses human faces to attempt to identify an individual or verify a person‟s claimed identity.”
Distribution Matching in Variational Inference (2018, background): “, 2003) or for dimensionality reduction, denoising and visualization (Smith, 2002).”
Predicting Diabetes Mellitus With Machine Learning Techniques (2018, background): “The eigenvalues are sorted from large to small, representing the variance of the observed variables explained by K principal components, respectively (Smith, 2002).”
Infrared Thermography for Temperature Measurement and Non-Destructive Testing (2014, methodology): “In [114], the steps for applying this method to a set of data are described, and in [115], the effectiveness of this method for reducing thermographic data is shown.”
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, background): “If further details are required, please find a detailed tutorial of the PCA in [48].”
Generation of a realistic 3D sand assembly using X‐ray micro‐computed tomography and spherical harmonic‐based principal component analysis (2017, background): “reducing such a redundancy and identifying patterns of this high-dimensional coefficient matrix [29, 30].”