matlab中tsne函数,t-Distributed Stochastic Neighbor Embedding

'euclidean' — Euclidean

distance.

'seuclidean' —

Standardized Euclidean distance. Each coordinate

difference between rows in X

and the query matrix is scaled by dividing by the

corresponding element of the standard deviation

computed from

S = std(X,'omitnan').

'cityblock' — City block

distance.

'chebychev' — Chebychev

distance, which is the maximum coordinate difference.

'minkowski' — Minkowski

distance with exponent 2. This is the same as Euclidean distance.

'mahalanobis' —

Mahalanobis distance, computed using the positive

definite covariance matrix

cov(X,'omitrows').

'cosine' — 1 minus the cosine

of the included angle between observations (treated as vectors).

'correlation' — One minus

the sample linear correlation between observations (treated as sequences

of values).

'spearman' — One minus the

sample Spearman's rank correlation between observations (treated as

sequences of values).

'hamming' — Hamming distance,

which is the percentage of coordinates that differ.

'jaccard' — One minus the

Jaccard coefficient, which is the percentage of nonzero coordinates

that differ.

custom distance function — A distance function

specified using @ (for example, @distfun).

For details, see More About.

总结

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