Tell me more ×  Gaussian mixture models (GMM) can be seen as the
probabilistic counterparts of the k-means clustering algorithm.
Weighted k-means takes a set of weighted samples and arranges
the centroids according to weighted means of the data clusters,
where the weights are the weights of the samples. I wonder if
there is a GMM-like probabilistic counterpart of weighted
k-means.  share|improve this question add comment  3 Answers
pKNN+AL (Jain and Kapoor, 2009) is a probabilistic modification
of the KNN classifier. Given a set of points $\{x_1, \ldots,
x_n\}$ from $\mathbb{R}^d$, labels $\{y_1, \ldots, y_n\}$ from
$[1,C]$, and a Mercer kernel $K$, the probability of $x$
belonging to class $c$ is  $$\frac{\frac{1}{n_c} \sum_{\{i : y_i
= c\}} K(x, x_i)}{\sum_{t=1}^C \frac{1}{n_t} \sum_{\{i : y_i =
c\}} K(x, x_i)}$$  where $n_c$ is the number of $x_i$ that
belong to class $c$. It is also an active learning algorithm and
comes with a MATLAB implementation.  share|improve this answer I
think our notion of the t