  Feature learning in the presence of a mixed type of variables, numerical and
categorical types, is an important issue for related modeling problems. For
simple neighborhood queries under mixed data space, standard practice is to
consider numerical and categorical variables separately and combining them
based on some suitable distance functions. Alternatives, such as Kernel
learning or Principal Component do not explicitly consider the inter-dependence
structure among the mixed type of variables. In this work, we propose a novel
strategy to explicitly model the probabilistic dependence structure among the
mixed type of variables by an undirected graph. Spectral decomposition of the
graph Laplacian provides the desired feature transformation. The Eigen spectrum
of the transformed feature space shows increased separability and more
prominent clusterability among the observations. The main novelty of our paper
lies in capturing interactions of the mixed feature type in an unsupervised
framework using a graphical model. We numerically validate the implications of
the feature learning strategy
