Principal Component Analysis

Ke-Lin Du () and M. N. S. Swamy
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Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 13 in Neural Networks and Statistical Learning, 2019, pp 373-425 from Springer

Abstract: Abstract Subspace learning techniques project high-dimensional data onto low-dimensional spaces. They are typically unsupervised. Well-known subspace learning algorithms are PCA, ICA, locality-preserving projection, and NMF. Discriminant analysis is a supervised subspace learning method and uses the data class label information. PCA is a classical statistical method for signal processing and data analysis. It is a feature extractor in the neural network processing setting, and is related to eigenvalue decomposition and singular value decomposition. This chapter introduces PCA, and the associated methods such as minor component analysis, generalized eigenvalue decomposition, singular value decomposition, factor analysis, and canonical correlation analysis.

Date: 2019
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DOI: 10.1007/978-1-4471-7452-3_13

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