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The analysis of mixed data has been raising challenges in statistics andmachine learning. One of two most prominent challenges is to develop newstatistical techniques and methodologies to effectively handle mixed data bymaking the data less heterogeneous with minimum loss of information. The otherchallenge is that such methods must be able to apply in large-scale tasks whendealing with huge amount of mixed data. To tackle these challenges, weintroduce parameter sharing and balancing extensions to our recent model, themixed-variate restricted Boltzmann machine (MV.RBM) which can transformheterogeneous data into homogeneous representation. We also integratestructured sparsity and distance metric learning into RBM-based models. Ourproposed methods are applied in various applications including latent patientprofile modelling in medical data analysis and representation learning forimage retrieval. The experimental results demonstrate the models perform betterthan baseline methods in medical data and outperform state-of-the-art rivals inimage dataset.

Statistical Latent Space Approach for Mixed Data Modelling and Applications