  The paper has been withdrawn since more effective experiments should be
completed.
  Auto-encoders (AE) has been widely applied in different fields of machine
learning. However, as a deep model, there are a large amount of learnable
parameters in the AE, which would cause over-fitting and slow learning speed in
practice. Many researchers have been study the intrinsic structure of AE and
showed different useful methods to regularize those parameters. In this paper,
we present a novel regularization method based on a clustering algorithm which
is able to classify the parameters into different groups. With this
regularization, parameters in a given group have approximate equivalent values
and over-fitting problem could be alleviated. Moreover, due to the competitive
behavior of clustering algorithm, this model also overcomes some intrinsic
problems of clustering algorithms like the determination of number of clusters.
Experiments on handwritten digits recognition verify the effectiveness of our
novel model.
