  For better clustering performance, appropriate representations are critical.
Although many neural network-based metric learning methods have been proposed,
they do not directly train neural networks to improve clustering performance.
We propose a meta-learning method that train neural networks for obtaining
representations such that clustering performance improves when the
representations are clustered by the variational Bayesian (VB) inference with
an infinite Gaussian mixture model. The proposed method can cluster unseen
unlabeled data using knowledge meta-learned with labeled data that are
different from the unlabeled data. For the objective function, we propose a
continuous approximation of the adjusted Rand index (ARI), by which we can
evaluate the clustering performance from soft clustering assignments. Since the
approximated ARI and the VB inference procedure are differentiable, we can
backpropagate the objective function through the VB inference procedure to
train the neural networks. With experiments using text and image data sets, we
demonstrate that our proposed method has a higher adjusted Rand index than
existing methods do.
