it is actually a second order step afaik. the diagonal of the hessian is given by the cluster sizes.



Robert Layton <notifications@github.com> schrieb:

>> -the distance of each points to the nearest centroid, is not as good
>as
>> -the :class:`KMeans` algorithm.
>> +which uses mini-batches to reduce the computation time. Mini-batches
>are
>> +randomly chosen subsets of data, which are recomputed in each
>training
>> +iteration. These mini-batches drastically reduce the amount of
>computation
>> +required to converge to a local solution. In contrast to other
>algorithms
>> +that reduce the convergence time of k-means, mini-batch k-means
>produces
>> +results that are generally only slightly worse than the standard
>algorithm.
>> +
>> +The algorithm iterates between two major steps, similar to vanilla
>k-means.
>> +In the first step, `b` samples are drawn randomly from the dataset,
>to form
>> +a mini-batch. These are then assigned to the nearest centroid. In
>the second
>> +step, the centroids are updated. In contrast to k-means, this is
>done on a
>> +per-sample basis. For each sample in the mini-batch, the assigned
>centroid
>> +is updated by taking a gradient step based on the sample and the
>previous
>> +centroid. The learning rate for this updating reduces over time.
>These steps
>
>Right. Would that be a gradient step on the objective function though?
>
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