@inbook{e70d0d1cccce4c13af8c1f9cec62594e,
title = "Principal Component Analysis in Noise Reduction and Beamforming",
abstract = "Principal component analysis (PCA) is by far the most popular and useful dimensionality reduction technique that one can find in the literature. The objective of PCA is the reduction of the dimension of a random signal vector from M to P, where P≪ M, with little loss of the useful information. It does so by preserving the variability of the signal as much as possible, where the new variables are uncorrelated. In this chapter, we show how PCA can be applied to noise reduction and beamforming.",
keywords = "Beamforming, Dimensionality reduction, Noise reduction, Principal component analysis (PCA)",
author = "Jacob Benesty and Gongping Huang and Jingdong Chen and Ningning Pan",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2024",
doi = "10.1007/978-3-031-36974-2_4",
language = "英语",
series = "Springer Topics in Signal Processing",
publisher = "Springer Science and Business Media B.V.",
pages = "57--85",
booktitle = "Springer Topics in Signal Processing",
}