Poster
in
Workshop: Interpretable AI: Past, Present and Future
Isometry pursuit
Samson Koelle · Marina Meila
Abstract:
Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices.It consists of a novel normalization method followed by multitask basis pursuit.Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries.We provide theoretical and experimental results justifying this method.It appears to be more accurate than greedy search and more efficient than brute force search.
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