  Clustering is an essential problem in machine learning and data mining. One
vital factor that impacts clustering performance is how to learn or design the
data representation (or features). Fortunately, recent advances in deep
learning can learn unsupervised features effectively, and have yielded state of
the art performance in many classification problems, such as character
recognition, object recognition and document categorization. However, little
attention has been paid to the potential of deep learning for unsupervised
clustering problems. In this paper, we propose a deep belief network with
nonparametric clustering. As an unsupervised method, our model first leverages
the advantages of deep learning for feature representation and dimension
reduction. Then, it performs nonparametric clustering under a maximum margin
framework -- a discriminative clustering model and can be trained online
efficiently in the code space. Lastly model parameters are refined in the deep
belief network. Thus, this model can learn features for clustering and infer
model complexity in an unified framework. The experimental results show the
advantage of our approach over competitive baselines.
