We present new guidelines for choosing hyperparameters for t-SNE and an evaluation comparing these guidelines to current ones.These guidelines include a proposed empirically optimum guideline derived from a t-SNE hype...We present new guidelines for choosing hyperparameters for t-SNE and an evaluation comparing these guidelines to current ones.These guidelines include a proposed empirically optimum guideline derived from a t-SNE hyperparameter grid search over a large collection of data sets.We also introduce a new method to featurize data sets using graph-based metrics called scagnostics;we use these features to train a neural network that predicts optimal t-SNE hyperparameters for the respective data set.This neural network has the potential to simplify the use of t-SNE by removing guesswork about which hyperparameters will produce the best embedding.We evaluate and compare our neural network-derived and empirically optimum hyperparameters to several other t-SNE hyperparameter guidelines from the literature on 68 data sets.The hyperparameters predicted by our neural network yield embeddings with similar accuracy as the best current t-SNE guidelines.Using our empirically optimum hyperparameters is simpler than following previously published guidelines but yields more accurate embeddings,in some cases by a statistically significant margin.We find that the useful ranges for t-SNE hyperparameters are narrower and include smaller values than previously reported in the literature.Importantly,we also quantify the potential for future improvements in this area:using data from a grid search of t-SNE hyperparameters we find that an optimal selection method could improve embedding accuracy by up to two percentage points over the methods examined in this paper.展开更多
基金We thank Kevin Merchant,David Slater,and Reed Gordon-Sarney for their technical support and thoughtful discussion.
文摘We present new guidelines for choosing hyperparameters for t-SNE and an evaluation comparing these guidelines to current ones.These guidelines include a proposed empirically optimum guideline derived from a t-SNE hyperparameter grid search over a large collection of data sets.We also introduce a new method to featurize data sets using graph-based metrics called scagnostics;we use these features to train a neural network that predicts optimal t-SNE hyperparameters for the respective data set.This neural network has the potential to simplify the use of t-SNE by removing guesswork about which hyperparameters will produce the best embedding.We evaluate and compare our neural network-derived and empirically optimum hyperparameters to several other t-SNE hyperparameter guidelines from the literature on 68 data sets.The hyperparameters predicted by our neural network yield embeddings with similar accuracy as the best current t-SNE guidelines.Using our empirically optimum hyperparameters is simpler than following previously published guidelines but yields more accurate embeddings,in some cases by a statistically significant margin.We find that the useful ranges for t-SNE hyperparameters are narrower and include smaller values than previously reported in the literature.Importantly,we also quantify the potential for future improvements in this area:using data from a grid search of t-SNE hyperparameters we find that an optimal selection method could improve embedding accuracy by up to two percentage points over the methods examined in this paper.
