Fig. 1: Overview: assessment of embeddings generated by neighbor embedding methods, illustrated with image data.

a We use a standard pre-trained convolutional neural network (CNN) to obtain features of image samples from the CIFAR10 dataset, and then visualize the features using a neighbor embedding method, specifically t-SNE. b Basic ideas of singularity scores and perturbation scores. c t-SNE tends to embed image features into separated clusters even for images with ambiguous semantic meanings (as quantified by higher entropies of predicted class probabilities by the CNN). Perturbation scores identify the embedding points that have ambiguous class membership but less visual uncertainty. d An incorrect choice of perplexity leads to visual fractures (FI discontinuity), which is more severe with a smaller perplexity. We recommend choosing the perplexity no smaller than the elbow point. Source data are provided as a Source Data file.