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Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction
[article]
2016
arXiv
pre-print
We reframe linear dimensionality reduction as a problem of Bayesian inference on matrix manifolds. This natural paradigm extends the Bayesian framework to dimensionality reduction tasks in higher dimensions with simpler models at greater speeds. Here an orthogonal basis is treated as a single point on a manifold and is associated with a linear subspace on which observations vary maximally. Throughout this paper, we employ the Grassmann and Stiefel manifolds for various dimensionality reduction
arXiv:1606.04478v1
fatcat:5tq3pjvarzcvdpkctgmboiwtqe