4. Unsupervised Metric Learning

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).

4.1. Algorithms

4.1.1. Covariance

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).

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)


[1]. On the Generalized Distance in Statistics, P.C.Mahalanobis, 1936.