Deep Variational Sufficient Dimensionality Reduction

Understand

We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved.

• We propose DVSDR, a deep variational approach for sufficient dimensionality reduction.
• The deep structure in our model has a bottleneck that represent the low-dimensional embedding of the data.
• We explain the SDR problem using graphical models and use the framework of variational autoencoders to maximize the lower bound of the log-likelihood of the joint distribution of the observation and label.