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Unsupervised Metric Learning
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Unsupervised metric learning algorithms only take as input an (unlabeled)
dataset `X`. For now, in metric-learn, there only is `Covariance`, which is a
simple baseline algorithm (see below).
Algorithms
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.. _covariance:
Covariance
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`Covariance` does not "learn" anything, rather it calculates
the covariance matrix of the input data. This is a simple baseline method.
It can be used for ZCA whitening of the data (see the Wikipedia page of
`whitening transformation `_).
.. topic:: Example Code:
::
from metric_learn import Covariance
from sklearn.datasets import load_iris
iris = load_iris()['data']
cov = Covariance().fit(iris)
x = cov.transform(iris)
.. topic:: References:
.. [1] On the Generalized Distance in Statistics, P.C.Mahalanobis, 1936