Entropic Wasserstein component analysis

A Collas, T Vayer, R Flamary… - 2023 IEEE 33rd …, 2023 - ieeexplore.ieee.org
2023 IEEE 33rd International Workshop on Machine Learning for …, 2023ieeexplore.ieee.org
Dimension reduction (DR) methods provide systematic approaches for analyzing high-
dimensional data. A key requirement for DR is to incorporate global dependencies among
original and embedded samples while preserving clusters in the embedding space. To
achieve this, we combine the principles of optimal transport (OT) and principal component
analysis (PCA). Our method seeks the best linear subspace that minimizes reconstruction
error using entropic OT, which naturally encodes the neighborhood information of the …
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original and embedded samples while preserving clusters in the embedding space. To achieve this, we combine the principles of optimal transport (OT) and principal component analysis (PCA). Our method seeks the best linear subspace that minimizes reconstruction error using entropic OT, which naturally encodes the neighborhood information of the samples. From an algorithmic standpoint, we propose an efficient block-majorization-minimization solver over the Stiefel manifold. Our experimental results demonstrate that our approach can effectively preserve high-dimensional clusters, leading to more interpretable and effective embeddings. Python code of the algorithms and experiments is available online 1 . 1 https://github.com/antoinecollas/Entropic_Wasserstein_Component_Analysis
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