  The excellent performance of representation learning of autoencoders have
attracted considerable interest in various applications. However, the structure
and multi-local collaborative relationships of unlabeled data are ignored in
their encoding procedure that limits the capability of feature extraction. This
paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists
of novel multi-local collaborative representation RBM (mcrRBM) and multi-local
collaborative representation GRBM (mcrGRBM) models. Here, the Locality
Sensitive Hashing (LSH) method is used to divide the input data into
multi-local cross blocks which contains multi-local collaborative relationships
of the unlabeled data and features since the similar multi-local instances and
features of the input data are divided into the same block. In mcrRBM and
mcrGRBM models, the structure and multi-local collaborative relationships of
unlabeled data are integrated into their encoding procedure. Then, the local
hidden features converges on the center of each local collaborative block.
Under the collaborative joint influence of each local block, the proposed MC-AE
has powerful capability of representation learning for unsupervised clustering.
However, our MC-AE model perhaps perform training process for a long time on
the large-scale and high-dimensional datasets because more local collaborative
blocks are integrate into it. Five most related deep models are compared with
our MC-AE. The experimental results show that the proposed MC-AE has more
excellent capabilities of collaborative representation and generalization than
the contrastive deep models.
