  The analysis of mixed data has been raising challenges in statistics and
machine learning. One of two most prominent challenges is to develop new
statistical techniques and methodologies to effectively handle mixed data by
making the data less heterogeneous with minimum loss of information. The other
challenge is that such methods must be able to apply in large-scale tasks when
dealing with huge amount of mixed data. To tackle these challenges, we
introduce parameter sharing and balancing extensions to our recent model, the
mixed-variate restricted Boltzmann machine (MV.RBM) which can transform
heterogeneous data into homogeneous representation. We also integrate
structured sparsity and distance metric learning into RBM-based models. Our
proposed methods are applied in various applications including latent patient
profile modelling in medical data analysis and representation learning for
image retrieval. The experimental results demonstrate the models perform better
than baseline methods in medical data and outperform state-of-the-art rivals in
image dataset.
