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What is Principal component analysis (PCA)?

A dimensionality-reduction method that represents data with many variables using fewer variables while losing as little information as possible. It takes the direction of greatest variance in the data as the first principal component, the direction of greatest variance orthogonal to it as the second, and so on, and represents the data with only the leading components. Mathematically this amounts to finding the eigenvectors of the data's covariance matrix. It is used for visualizing data, removing noise and preprocessing before training.

Japanese name: 主成分分析(PCA)

Principal component analysis (PCA) appears in 1 practice question across 1 topic, including Machine Learning Basics.

Field: Machine Learning Basics

Try it: questions about Principal component analysis (PCA)

Which of the following describes principal component analysis (PCA)?

  1. ASelecting the few features that best predict the correct-answer labels and discarding all the other features to make the data smaller
  2. BDividing data into a predetermined number of groups so that similar items fall in the same group
  3. CFinding new, mutually orthogonal axes in order, starting from the direction in which the spread (variance) of the data is largest, and representing the data with few axes
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Answer: C. Finding new, mutually orthogonal axes in order, starting from the direction in which the spread (variance) of the data is largest, and representing the data with few axes

Principal component analysis is a representative dimensionality-reduction method that represents data with many variables using fewer variables while losing as little information as possible. It finds, in order, the direction of greatest variance in the data as the first principal component, the direction of greatest variance orthogonal to it as the second principal component, and so on, and represents the data with only the leading components. It is a kind of unsupervised learning that uses no correct-answer labels and is used for visualizing data and for preprocessing before training. If the variables have different units or scales, the result is pulled toward variables with large values, so the data is often standardized first.

Principal component analysis (PCA) in the explanations

Questions that mention Principal component analysis (PCA) (1)

  1. Which of the following describes principal component analysis (PCA)?

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Related terms

Unsupervised learning Activation function Confusion matrix Convolutional neural network (CNN) Cross-validation Gradient descent k-means clustering Overfitting Precision and recall Recurrent neural network (RNN)

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