  Variation Autoencoder (VAE) has become a powerful tool in modeling the
non-linear generative process of data from a low-dimensional latent space.
Recently, several studies have proposed to use VAE for unsupervised clustering
by using mixture models to capture the multi-modal structure of latent
representations. This strategy, however, is ineffective when there are outlier
data samples whose latent representations are meaningless, yet contaminating
the estimation of key major clusters in the latent space. This exact problem
arises in the context of resting-state fMRI (rs-fMRI) analysis, where
clustering major functional connectivity patterns is often hindered by heavy
noise of rs-fMRI and many minor clusters (rare connectivity patterns) of no
interest to analysis. In this paper we propose a novel generative process, in
which we use a Gaussian-mixture to model a few major clusters in the data, and
use a non-informative uniform distribution to capture the remaining data. We
embed this truncated Gaussian-Mixture model in a Variational AutoEncoder
framework to obtain a general joint clustering and outlier detection approach,
called tGM-VAE. We demonstrated the applicability of tGM-VAE on the MNIST
dataset and further validated it in the context of rs-fMRI connectivity
analysis.

Machine Learning;Machine Learning