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Everything about Principal component analysis
Method of data analysis
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Principal component analysisA 5-minute orientation
- Skim a recent paper: Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluatedInfluential work since 2022
- Pick up a classic book: PCA-STREAMThe most-reprinted book on the subject
- Read the landmark paper: Two-dimensional pca: a new approach to appearance-based face representation and recognitionCited 2,854 times
- Go sideways: EigenfaceA closely related topic
- Go sideways: Modes of variationA closely related topic
📖 Definitions
Symbol ISO 639-3 language code for Santa Inés Ahuatempan Popoloca.
🏆 Landmark papers (most cited)
Two-dimensional pca: a new approach to appearance-based face representation and recognition
PCA versus LDA
Principal components analysis (PCA)
pcaMethods—a bioconductor package providing PCA methods for incomplete data
EEG signal classification using PCA, ICA, LDA and support vector machines
Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective
Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)
Recursive PCA for adaptive process monitoring
🆕 Influential since 2022
Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated
Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF
A spatial distribution – Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil
Scaled PCA: A New Approach to Dimension Reduction
Tutorial on PCA and approximate PCA and approximate kernel PCA
📚 Books worth reading
PCA-STREAMNicolas Bourriaud · 2019- 📕Tours architecturePhilippe Chiambaretta · 2007
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Eigenface An eigenface (/ˈaɪɡən-/ EYE-gən-) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach…
- Modes of variation In statistics, modes of variation are a continuously indexed set of vectors or functions that are centered at a mean and are used to depict the variation…
- Whitening transformation A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix…
- Factor analysis Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved…
- Canonical correlation In statistics, canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices…
- Covariance matrix In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance…
- Sparse PCA Sparse principal component analysis (SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis of multivariate…
- Principal component regression In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form…