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.
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