A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal SubspacesDownload PDF

Published: 23 Nov 2022, Last Modified: 14 Jul 2024OPT 2022 PosterReaders: Everyone
Keywords: representation learning, deep learning, PCA
TL;DR: algorithm amenable to deep learning that learns a principal subspace from sample entries
Abstract: In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace is represented by a neural network, and hence can bescaled to datasets with an effectively infinite number of rows and columns. Our method consistsin defining a loss function whose minimizer is the desired principal subspace, and constructing agradient estimate of this loss whose bias can be controlled.
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