71 citations · 123 across the 6 of their papers we have counts for
4 papers · 1 filter
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
David Hong, Kyle Gilman, Laura Balzano +1
Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common i…
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
David Hong, Fan Yang, Jeffrey A. Fessler +1
Modern data are increasingly both high-dimensional and heteroscedastic. This paper considers the challenge of estimating underlying principal components from high-dimensional data…
Asymptotic performance of PCA for high-dimensional heteroscedastic data
David Hong, Laura Balzano, Jeffrey A. Fessler
Principal Component Analysis (PCA) is a classical method for reducing the dimensionality of data by projecting them onto a subspace that captures most of their variation. Effective…
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
David Hong, Laura Balzano, Jeffrey A. Fessler
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…