  Motivated by the ever-increasing demands for limited communication bandwidth
and low-power consumption, we propose a new methodology, named joint
Variational Autoencoders with Bernoulli mixture models (VAB), for performing
clustering in the compressed data domain. The idea is to reduce the data
dimension by Variational Autoencoders (VAEs) and group data representations by
Bernoulli mixture models (BMMs). Once jointly trained for compression and
clustering, the model can be decomposed into two parts: a data vendor that
encodes the raw data into compressed data, and a data consumer that classifies
the received (compressed) data. In this way, the data vendor benefits from data
security and communication bandwidth, while the data consumer benefits from low
computational complexity. To enable training using the gradient descent
algorithm, we propose to use the Gumbel-Softmax distribution to resolve the
infeasibility of the back-propagation algorithm when assessing categorical
samples.
