  In this paper, we present a novel unsupervised feature learning architecture,
which consists of a multi-clustering integration module and a variant of RBM
termed multi-clustering integration RBM (MIRBM). In the multi-clustering
integration module, we apply three unsupervised K-means, affinity propagation
and spectral clustering algorithms to obtain three different clustering
partitions (CPs) without any background knowledge or label. Then, an unanimous
voting strategy is used to generate a local clustering partition (LCP). The
novel MIRBM model is a core feature encoding part of the proposed unsupervised
feature learning architecture. The novelty of it is that the LCP as an
unsupervised guidance is integrated into one step contrastive divergence (CD1)
learning to guide the distribution of the hidden layer features. For the
instance in the same LCP cluster, the hidden and reconstructed hidden layer
features of the MIRBM model in the proposed architecture tend to constrict
together in the training process. Meanwhile, each LCP center tends to disperse
from each other as much as possible in the hidden and reconstructed hidden
layer during training. The experiments demonstrate that the proposed
unsupervised feature learning architecture has more powerful feature
representation and generalization capability than the state-of-the-art graph
regularized RBM (GraphRBM) for clustering tasks in the Microsoft Research Asia
Multimedia (MSRA-MM)2.0 dataset.
