Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection

arXiv:2211.09321v2 Announce Type: replace-cross
Abstract: Nonlinear dimensionality reduction lacks interpretability due to the absence of source features in low-dimensional embedding space. We propose an interpretable method featMAP to preserve source features by tangent space embedding. The core of our proposal is to utilize local singular value decomposition (SVD) to approximate the tangent space which is embedded to low-dimensional space by maintaining the alignment. Based on the embedding tangent space, featMAP enables the interpretability by locally demonstrating the source features and …

abstract approximation arxiv core cs.cv cs.lg dimensionality embedding feature features interpretability low manifold projection singular space svd type value