Towards a theoretical analysis of PCA for heteroscedastic data
Towards a theoretical analysis of PCA for heteroscedastic data
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data. PCA performs well in the presence of moderate noise and even with missing data, …