Detection of Health Data Based on Gaussian Mixture Generative Model

Abstract

Sports bracelet provides rich information for a comprehensive understanding of people’s physical health in the context of the popularity of smart wearable devices. However, some unknown outliers inevitably exist in the provided multidimensional activity data and the detection of outliers is necessary. Due to the “dimension disaster”, it is difficult to estimate the density by traditional methods, leading to poor detection performance. Aiming at the problem, a method of detecting health data is utilized, called Gaussian mixture generative model (GMGM). The model uses a variational autoencoder (VAE) to train the original data and latent features can be extracted by minimizing the reconstruction error. Then, the deep belief network (DBN) is used to predict the sample mixture membership with the help of potential distribution and the extracted features. Next, VAE, DBN and Gaussian mixture model (GMM) are optimized together to avoid the influence of model decoupling. Finally, the density of each sample point is predicted by GMM and the samples whose density is higher than the threshold in the training stage will be viewed as outliers. The performance of the GMGM is verified on the ODDS standard datasets. The results show that the model achieves a promotion of 5.5 percentage points for AUC score compared with deep autoencoding Gaussian mixture model (DAGMM). Finally, the experimental results on real datasets also show the effectiveness of GMGM.

Keywords

• |variational autoencoder (vae)|deep brief network (dbn)|gaussian mixture model (gmm)|health data|anomaly detection