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stat.ME2016
PCA: High Dimensional Exponential Family PCA
Lydia T. Liu, Edgar Dobriban, Amit Singer
Many applications, such as photon-limited imaging and genomics, involve large datasets with noisy entries from exponential family distributions. It is of interest to estimate the c…
math.ST2016★ 27 cited
Sharp detection in PCA under correlations: all eigenvalues matter
Edgar Dobriban
Principal component analysis (PCA) is a widely used method for dimension reduction. In high dimensional data, the "signal" eigenvalues corresponding to weak principal components (P…