  Representation of data on mixed variables, numerical and categorical types to
get suitable feature map is a challenging task as important information lies in
a complex non-linear manifold. The feature transformation should be able to
incorporate marginal information of the individual variables and complex
cross-dependence structure among the mixed type of variables simultaneously. In
this work, we propose a novel nonlinear Deep Encoder-Decoder framework to
capture the cross-domain information for mixed data types. The hidden layers of
the network connect the two types of variables through various non-linear
transformations to give latent feature maps. We encode the information on the
numerical variables in a number of hidden nonlinear units. We use these units
to recreate categorical variables through further nonlinear transformations. A
separate and similar network is developed switching the roles of the numerical
and categorical variables. The hidden representational units are stacked one
next to the others and transformed into a common space using a locality
preserving projection. The derived feature maps are used to explore the
clusters in the data. Various standard datasets are investigated to show nearly
the state of the art performance in clustering using the feature maps with
simple K-means clustering.
