Principal Component Analysis (PCA)
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Summary
The goal of PCA is to reduce the dimensionality of the data while retaining as much as possible of the variation present in the original dataset.
- Published
- 2014-06-02
- Cited by
- 27,631
- References
- 4
- Access
- Open access
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2534141
References
- Functional Data Analysis
- Population Structure and Eigenanalysis
- What is principal component analysis?
- Selecting the number of principal components: estimation of the true rank of a noisy matrix
- Über lineare Methoden in der Wahrscheinlichkeitsrechnung
- Analysis of Symbolic Data
- Ionospheric precursors of earthquakes
- Analyse des données
- Subset Correspondence Analysis
- Chemometrics, mathematics and statistics in chemistry
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Class and lifestyle 'lock-in' among middle-aged and older men: a Multiple Correspondence Analysis of the British Regional Heart Study.
- Independent Component Analysis: A Tutorial Introduction
- Applied Multivariate Statistical Analysis
- Pattern Recognition and Machine Learning
- Correspondence analysis in practice
- Statistical Learning with Sparsity: The Lasso and Generalizations
- Principal Component Analysis on Human Development Indicators of China
- Factor Analysis: Statistical Methods and Practical Issues
- Applied Factor Analysis
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- Classification multi-modèles des images dans les bases Hétérogènes. (Multi-model image classification in heterogeneous databases)
- Intrinsic Dimension Estimation by Maximum Likelihood in Probabilistic PCA
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- Using principal component analysis for early fault detection in Louisville water distribution system
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