Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Clustering is among the most fundamental tasks in machine learning and artificial intelligence. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using the…

• NN
  National Natural Science Foundation of China

  Awards: 61620106002, 61271376
• BI
  Beijing Institute of Technology

  Award: 61271376