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).
from metric_learn import Covariance
from sklearn.datasets import load_iris
iris = load_iris()['data']
cov = Covariance().fit(iris)
x = cov.transform(iris)
References:
[1] | On the Generalized Distance in Statistics, P.C.Mahalanobis, 1936 |