Multi-manifold clustering: A graph-constrained deep nonparametric method
作者：
Highlights：
• Two new nonparametric methods are proposed to extend the application scope of the conventional Dirichlet process mixture model.
• A deep learning based nonparametric algorithm is proposed for the image generation and clustering task, in which the cluster number will not be pre-specified but estimated by model.
• A new deep neural network architecture named Graph-VAE has been presented for the manifold and posterior probability approximation.
• A mean field variational inference algorithm is derived, which incorporates the learning procedure with the Graph-VAE to handle the non-conjugated parameter estimation.
摘要
•Two new nonparametric methods are proposed to extend the application scope of the conventional Dirichlet process mixture model.•A deep learning based nonparametric algorithm is proposed for the image generation and clustering task, in which the cluster number will not be pre-specified but estimated by model.•A new deep neural network architecture named Graph-VAE has been presented for the manifold and posterior probability approximation.•A mean field variational inference algorithm is derived, which incorporates the learning procedure with the Graph-VAE to handle the non-conjugated parameter estimation.
论文关键词：Multi-manifold clustering,Image generation,Dirichlet process mixture model,Variational inference,Graph,Deep neural network
论文评审过程：Received 30 March 2018, Revised 9 March 2019, Accepted 24 April 2019, Available online 25 April 2019, Version of Record 30 April 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.04.029
