A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine

By Lijuan Cao, K. Chua, W. K. Chong, Heow Pueh Lee, Q. Gu. Neurocomputing, 2003

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A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “It is said that ICA works better if the data has been preprocessed with PCA [86].”
Anomaly Detection using One-Class Neural Networks (2018, background): “In recent times, several approaches of feature selection and feature extraction methods have been proposed for complex, high-dimensional data for use with OC-SVM [9, 28].”
EEG signal classification using PCA, ICA, LDA and support vector machines (2010, background): “Details are given in Cao et al. (2003), Duda, Hart, and Strok (2001).”
Kernel Methods in Finance (2008, background): “( Cao et al. 2003a, b) compared the performance of PCA, KPCA and independent component analysis (ICA) for feature extraction of different time series data sets (including real futures contracts).”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “Cao and Chong [36] compared the performance of applications of PCA, Kernel Principal Component Analysis, and ICA to a SVM for feature extraction to predict the stock price.”
Text Classification Algorithms: A Survey (2019, methodology): “kernel principal component analysis (KPCA) is another dimensionality reduction method that generalizes linear PCA into the nonlinear case by using the kernel method [71].”