Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection
This work addresses the lack of interpretability in dimensionality reduction for researchers and practitioners in machine learning, offering a novel approach to feature preservation.
The authors tackled the problem of interpretability in nonlinear dimensionality reduction by proposing featMAP, a method that preserves source features through tangent space embedding and anisotropic projection, achieving explicit feature distinction in digit classification, object detection, and MNIST adversarial examples.
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 feature importance. Furthermore, featMAP embeds the data points by anisotropic projection to preserve the local similarity and original density. We apply featMAP to interpreting digit classification, object detection and MNIST adversarial examples. FeatMAP uses source features to explicitly distinguish the digits and objects and to explain the misclassification of adversarial examples. We also compare featMAP with other state-of-the-art methods on local and global metrics.