  Unsupervised dimensionality reduction is one of the commonly used techniques
in the field of high dimensional data recognition problems. The deep
autoencoder network which constrains the weights to be non-negative, can learn
a low dimensional part-based representation of data. On the other hand, the
inherent structure of the each data cluster can be described by the
distribution of the intraclass samples. Then one hopes to learn a new low
dimensional representation which can preserve the intrinsic structure embedded
in the original high dimensional data space perfectly. In this paper, by
preserving the data distribution, a deep part-based representation can be
learned, and the novel algorithm is called Distribution Preserving Network
Embedding (DPNE). In DPNE, we first need to estimate the distribution of the
original high dimensional data using the $k$-nearest neighbor kernel density
estimation, and then we seek a part-based representation which respects the
above distribution. The experimental results on the real-world data sets show
that the proposed algorithm has good performance in terms of cluster accuracy
and AMI. It turns out that the manifold structure in the raw data can be well
preserved in the low dimensional feature space.
