Capturing the Denoising Effect of PCA via Compression Ratio

arXiv:2204.10888v2 Announce Type: replace
Abstract: Principal component analysis (PCA) is one of the most fundamental tools in machine learning with broad use as a dimensionality reduction and denoising tool. In the later setting, while PCA is known to be effective at subspace recovery and is proven to aid clustering algorithms in some specific settings, its improvement of noisy data is still not well quantified in general.
In this paper, we propose a novel metric called \emph{compression ratio} to capture the …

abstract algorithms analysis arxiv clustering compression cs.lg denoising dimensionality fundamental machine machine learning pca recovery tool tools type via