A Parameterless t-SNE for Faithful Cluster Embeddings from Prototype-based Learning and CONN Similarity
Josh Taylor, Erzsébet Merényi · ESANN 2021 proceedings · 2021
We propose an improvement to t-SNE which allows automated specification of its perplexity parameter using topological information about a data manifold revealed through neural prototype-based learning.This information is contained in the CONN (CONNectivity) similarity of neural prototypes, which expresses the strength (weakness) of topological connectivity at various points within the manifold.Experiments show that improvements, collectively called CONNt-SNE, are capable of producing meaningful and trustworthy low-dimensional embeddings without the need to heuristically optimize over (i.e., grid search) t-SNE's perplexity space.Data-driven perplexity determination improves our confidence that any structure appearing in the embeddings is valid and not merely an artifact of spurious parameterization.