  Unsupervised learning is becoming more and more important recently. As one of
its key components, the autoencoder (AE) aims to learn a latent feature
representation of data which is more robust and discriminative. However, most
AE based methods only focus on the reconstruction within the encoder-decoder
phase, which ignores the inherent relation of data, i.e., statistical and
geometrical dependence, and easily causes overfitting. In order to deal with
this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature
learning approach to improve the conventional AE. To the best of our knowledge,
our research is the first to utilize such exclusivity concept to cooperate with
feature extraction within AE. Moreover, in this paper we also make some
improvements to the stacked AE structure especially for the connection of
different layers from decoders, this could be regarded as a weight
initialization trial. The experimental results show that our proposed approach
can achieve remarkable performance compared with other related methods.
