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We derive a numerical strategy for dimension reducing preprocessing to facilitate machine learning on high-dimensional imaging data. Certain orthogonal projectors are designed to reduce data dimension such that essential information content is preserved for subsequent learning. This strategy is applied to clinical spectral-domain optical coherence tomography data of the human retina for automated identification tasks.
This is poster number 20 in Poster Session