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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: Phantom oscillations in principal component analysisInfluential work since 2022
- Pick up a classic book: Compositional Data Analysis in PracticeThe most-reprinted book on the subject
- Read the landmark paper: Principal component analysisCited 10,430 times
- Go sideways: EigenfaceA closely related topic
- Go sideways: Modes of variationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Principal component analysis
Principal component analysis
Robust principal component analysis?
Principal component analysis
Probabilistic Principal Component Analysis
Sparse Principal Component Analysis
Principal component analysis
Principal component analysis
🆕 Influential since 2022
📚 Books worth reading
- 📕Compositional Data Analysis in PracticeMichael Greenacre · 2018
- 📕Constrained Principal Component Analysis and Related TechniquesYoshio Takane · 2013
Applied Multiway Data AnalysisPieter M. Kroonenberg · 2007
Principal component analysisI. T. Jolliffe · 2006
Principal components analysisGeorge H. Dunteman · 1989
Principal component analysisI. T. Jolliffe · 1986
Independent component analysisAapo Hyvarinen · 2001- 📕Principal components analysis and its application to wind field pattern recognitionJ. J. Walton · 1978
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- 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…